A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy

arXiv:2608.07427 · cs.AI, cs.PF · Submitted 2026-08-07 · Read on arXiv

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Introduction to the show: ident: Paper Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy".

Jane: The paper was written by Bhavika Jalli, Nikhil Korati Prasanna and Jayanta Choudhury from Ericsson.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Paper summary: Tom: So we finally get to talk about this one, and the headline is simple. When you feed an LLM raw time-series numbers, a single monitoring window from a telecom cell site explodes into tens of thousands of tokens. Since inference energy scales with token count, you're paying a lot of energy to represent data that isn't even text. The paper's proposal is to show the model a picture of the data instead, and that single change does two things at once.

Jane: So render the time series as a 2D plot and feed that to a vision-language model? That's the whole trick.

Tom: That's the whole trick, and the numbers back it up. Across three architectures — Llama-3 point 2-90B-Vision, Qwen2 point 5-VL-72B, and Pixtral-12B — the image modality cuts input tokens by 3 point 6 to 10 point 4 times, and measured inference energy drops by 1 point 8 to 2 point 5 times. On the telecom dataset, that works out to roughly 7 point 2 megajoules saved per day at a deployment watching about 200 cells every fifteen minutes.

Lu: And the accuracy doesn't just hold; it improves. On the telecom anomaly detection task, the fine-tuned vision model gets 220 point 7 percent higher precision than the text-only version. It also beats LSTM and ARIMA baselines by over 144 percent, which is a strong statement on real operational data.

Meng: Right, so this isn't an efficiency-for-accuracy trade. The visual representation is actually better at capturing signal shapes because text serialization fragments the waveform across tokenization boundaries. Spikes and humps become spatial patterns you can see, rather than numbers scattered across thousands of tokens.

Lalam: And when you normalize energy by accuracy on the public benchmark, Pixtral improves by 20 point 6 times in J/F1. That's the kind of structural gain that compounds across millions of daily queries, which is exactly the setting the introduction describes.

Tom: There's also a feasibility angle that people might miss. At 24 KPIs, the text representation exceeds the 128K context window of most production models, so at that dimensionality the image pathway is the only way to run the workload at all. Let's go to page one, where they set up the macro pressure: data-center power growth and where the energy actually goes.

Page 1 of the paper: Tom: So we've got the thesis in place, and page one backs it up with the macro picture — the scale of the problem we're trying to solve. The paper cites US data-center power demand growing from 4 gigawatts in 2024 to 123 gigawatts by 2035. That's a thirty-fold increase in a decade, and it lands at a time when power executives already say grid capacity is their most critical challenge.

Jane: Seventy-two percent of them, according to the paper, with interconnection queues stretching to seven years. So you can't simply build your way out of this quickly.

Tom: Exactly, and that's why the paper frames reducing per-inference energy as an engineering prerequisite. It also points to where the energy actually goes: LLM inference accounts for over 90 percent of eye lifecycle power. Training gets the headlines, but inference runs continuously, and measurement studies show its energy scales linearly with input token count.

Lu: So token count is the lever you can actually pull. And for numerical time-series data, the token situation is absurd — a single 8-KPI window of 381 time points generates 46,000 to 60,000 tokens. That exceeds the context windows of production models and approaches the memory ceilings of edge GPUs.

Meng: The paper makes an important point that tokenization adds no representational value for numbers. You're burning energy on floating-point sequences that carry far less information density than natural language, and you're also introducing error risk from improper token patterns.

Lalam: So the page sets up the mismatch beautifully. It then lists the five contributions, and the one that anchors everything is the first direct energy comparison of LLM versus VLM inference for time-series anomaly detection across three vision encoder architectures.

Jane: The other contributions map to the rest of the paper — empirical measurements on public and telecom data, image compression as an extra lever, architecture guidance for edge deployment, and operational scaling implications.

Tom: And that gives us a clean map of the paper. Page two follows up by reviewing the prior work on token-energy coupling and the hardware constraints at the edge, which is where the argument gets really concrete.

Page 2 of the paper: Tom: Page two digs into the related work, and the first number sets the tone: pushing input length from 100 tokens to 900 tokens, at a fixed 100-token output, raises energy by 2 point 19 times. That isolates prefill cost — the phase where the model reads the prompt — as a major, independently actionable target.

Jane: And longer prompts don't just cost joules. The paper cites work showing they increase joules-to-first-token and reduce throughput by saturating the GPU. So the token budget affects latency and capacity, not just the electricity bill.

Tom: Now the paper is honest about the counterargument. VLMs draw more power per token because of the visual encoder overhead and the modality-fusion layers, so on a per-token basis they're the more expensive option. But earlier work shows visual tokens carry substantial redundancy — only a small fraction is essential for accurate responses.

Lu: And that same line of work demonstrates the upside: VLMs consume up to 36 times fewer tokens per variable while still beating text-based anomaly detection. There's also a cited result where few-shot visual prompting alone delivers up to 433 percent improvement over numerical approaches on deterministic reasoning tasks.

Meng: Because visual representations amplify coarse temporal structures — spikes, humps, oscillatory deviations — that raw numeric tokens obscure. The structure is spatially coherent in a picture, but text serialization shreds it across token boundaries.

Lalam: So on the evidence side, the visual advantage is plausible. Then the page brings it down to hardware, and this is where it gets urgent. A single RTX A6000 — representative of edge accelerators — hits out-of-memory errors beyond 60,000 tokens under 4-bit quantization, with 50,000 tokens at 0 point 7 GPU utilization as the safe ceiling.

Tom: And here's the crunch: the paper's text representation of that 8-KPI window lands right on top of that ceiling, while the visual modality needs only 5,442 to 16,800 tokens. So on a single-GPU edge deployment, going visual is about fitting the workload into the machine at all.

Jane: That reframes the whole paper. It's not a nice efficiency win; it's a deployment prerequisite. And the page closes by stating the central empirical question: does VLM token compression yield a net energy reduction that outweighs the higher per-token cost?

Tom: That question needs an experimental answer, which is exactly what page three provides — the rendering pipeline, the energy measurement framework, and the model selection strategy.

Page 3 of the paper: Tom: Page three is methodology, and the first decision is to render the time series as 2D raster images, with time on the horizontal axis and measurement values on the vertical. Crucially, they strip off tick marks, numeric labels, and gridlines, following the VLM4TS procedure. If you left the numbers in the image, the model would read those text artifacts instead of looking at the waveform.

Jane: That stripping is clever. It forces the vision model to attend to geometry rather than decoding text that's been baked into the pixels.

Tom: The paper's extension beyond VLM4TS is the stacked subplot composition for multivariate data. Instead of a single univariate panel, you get vertically stacked subplots, one per KPI, sharing a common time axis. That preserves inter-variable alignment while keeping each variable visually separate.

Lu: And there's a geometric justification that's worth unpacking. A multivariate series of dimension d traces a path on a d-dimensional manifold, and the stacked subplots form a visual cross-section of that manifold — each subplot contributes one coordinate of the instantaneous state vector. That gives the vision model a way to jointly attend to co-occurring morphological features across variables.

Meng: They also condition inference with structured prompts encoding the detection objective alongside domain-specific morphological primitives — spike, hump, oscillatory deviation — and their geometric attributes. So the model is steered toward waveform geometry rather than raw values.

Lalam: The energy measurement methodology is where the paper earns trust. They use the Zeus framework, reading cumulative hardware energy counters via NVML, which avoids the sampling error that comes with power polling. Instantaneous power is polled at 50-millisecond intervals as a secondary check, integrated with the trapezoidal rule.

Tom: And each configuration runs three independent passes of 256 output tokens after a warmup to stabilize GPU clocks. Multi-GPU setups sum energy across all devices. That's the kind of measurement hygiene that makes the later numbers believable.

Jane: Model selection deliberately spans three vision encoder strategies. Llama-3 point 2-90B-Vision uses cross-attention fusion with a fixed 6,404-token visual budget regardless of resolution. Qwen2 point 5-VL-72B applies dynamic-resolution patching that scales with image height. Pixtral-12B decomposes images into 16 by 16 pixel patches, giving the highest vision token counts.

Lu: That spread is the right call. If the efficiency result only held on one tokenization scheme, it wouldn't be a structural claim about visual representation. The three architectures give the paper its generality.

Tom: And the authors reassess token counts as image resolution grows with dimensionality, because stacked subplots scale pixel dimensions linearly with the number of variables. Page four shows what happens when those choices meet the public benchmark.

Page 4 of the paper: Tom: Page four opens with the public benchmark on realAWSCloudwatch, and the accuracy pattern is consistent across all three models. Image modality beats text everywhere, with mean F1 from 0 point 70 to 0 point 88 against 0 point 58 to 0 point 66 for text. The visual representation isn't just cheaper; it's more accurate on cloud infrastructure monitoring signals.

Jane: The energy numbers are dramatic. Per query, the image pathway cuts energy by 4 point 5 times on Llama, 13 point 8 times on Qwen, and 18 point 3 times on Pixtral. Pixtral drops from about 39,663 joules to 2,166 joules per query — that's the kind of reduction that changes capacity planning.

Tom: The paper introduces the J/F1 metric to combine those two dimensions — energy per unit of F1 score, lower being better. Pixtral's image pathway reaches 2,538 J/F1 versus 52,356 for text, a 20 point 6 times improvement, while delivering a mean F1 of 0 point 82 at the lowest absolute energy.

Lu: And Qwen's text configuration sits at 197,617 J/F1, which the paper calls operationally impractical for high-frequency monitoring. So the text pathway isn't uniformly bad; on some architectures, it's catastrophically bad.

Meng: That architecture-dependence comes from tokenizer behavior. Qwen and Pixtral split floating-point numbers into more sub-word tokens than Llama, which makes the visual pathway even more advantageous for those models. The tokenizer choice in the text baseline really matters.

Lalam: Then the page turns to the telecom dataset, and the token reductions land at 7 point 2 times for Llama's fixed visual budget, 10 point 4 times for Qwen's dynamic tiling, and 3 point 6 times for Pixtral's dense patches. The variation is itself informative for deployment planning.

Tom: The accuracy table is the centerpiece. The fine-tuned Llama vision model reaches precision 0 point 465 against 0 point 145 for the text-only LLM — that's the 220 point 7 percent improvement — while consuming 7 point 2 times fewer tokens. Its F1 comes in at 0 point 464 versus 0 point 185 for text.

Jane: And the detail that makes the result robust is that the zero-shot vision model already outperforms every text-based method, with F1 0 point 360 against 0 point 185 to 0 point 190 for the text LLM, LSTM, and ARIMA. So the visual representation itself provides the advantage, not just the fine-tuning.

Lu: Fine-tuning with LoRA pushes it further without changing token count or energy, because it only modifies attention weights. The accuracy uplift is effectively free in energy terms, which is a rare combination in this space.

Lalam: The page also hints at a scaling problem building in the background — token growth as more KPIs get stacked into the image. Page five makes that the main event.

Page 5 of the paper: Tom: Page five starts with token scaling as the KPI count grows, and the shape of that growth is architecture-dependent. Qwen's dynamic tiling scales most aggressively with image height, while Llama's fixed budget stays constant no matter how many subplots you stack. Pixtral sits in the middle with its patch-based approach.

Jane: That divergence becomes a practical problem for the text modality. At 24 KPIs, every text representation exceeds 128K tokens for all three models — roughly 2 point 9 times the token count at 8 KPIs. That blows past the context window of most production deployments.

Tom: And the consequence is lossy truncation. You literally cannot feed the full time series to the model, so you're throwing away data before the analysis even starts. The visual modality stays within standard limits, so it has no equivalent failure mode.

Lu: Then the paper measures energy on the realistic deployment configurations — Llama on four A100s, Qwen on two, Pixtral on a single A6000. The reductions are 2 point 5 times, 1 point 8 times, and 2 point 5 times respectively. The gap narrows relative to the token savings because the visual encoder adds a fixed overhead per query.

Meng: But the net balance still decisively favors the image at every scale tested. At operational scale, 209 cells queried every 15 minutes means about 20,064 queries a day, and the savings compound to roughly 7 point 2 megajoules per day per model.

Lalam: The image compression experiment is the most practical part of the page. Dropping from 150 DPI to 75 DPI cuts vision tokens by 70 percent and inference energy by 24 percent, with F1 actually nudging up slightly, from 0 point 347 to 0 point 354. Even at 50 DPI, roughly one-ninth the pixel area, F1 stays within 2 percent of baseline.

Tom: And JPEG at 75 DPI produces identical token counts to PNG with negligible accuracy impact. So operators get a free tuning dial on resolution — you can trade image quality for energy without retraining the model.

Jane: The paper is also honest about why energy savings trail token savings: the 256 output tokens are a fixed cost that dominates at low input counts. That analytical note stops readers from expecting a one-to-one mapping.

Lu: Those results hand the paper its final arguments — energy savings that hold at scale, accuracy that survives aggressive compression, and architecture-specific guidance for choosing a model. That's the discussion we'll wrap with in the final segment.

Conclusion: Tom: Let's close the loop. Across public and telecom datasets, the paper shows that presenting time-series as images instead of text cuts input tokens by 3 point 6 to 10 point 4 times and measured inference energy by 1 point 8 to 2 point 5 times, with accuracy improving at the same time. The headline numbers hold up under scrutiny.

Jane: The fine-tuned Llama vision model hits F1 0 point 464 against 0 point 185 for text, precision improves by 220 point 7 percent, and Pixtral's 20 point 6 times J/F1 gain on the public benchmark gives deployment teams a concrete selection criterion. Those are the numbers to remember.

Tom: The architecture guidance falls out of the measurements. Llama's fixed token budget gives predictable energy for capacity planning. Qwen achieves the strongest compression on standard inputs but scales aggressively with image height. Pixtral preserves spatial fidelity at a more modest 3 point 6 times reduction.

Lu: And the 75 DPI result is a gift to edge engineers — a 24 percent energy cut with no accuracy cost and no retraining. That's the kind of operational detail that makes a paper useful on Monday morning.

Meng: Stepping back, the paper's real contribution is treating energy as a first-class engineering constraint and measuring it directly. The results say that for serious time-series analysis in an energy-constrained environment, the visual modality is the appropriate tool.

Lalam: The authors are also honest about the boundaries. Single-GPU measurements, only three architectures, fixed 256-token outputs, one telecom operator. Multi-GPU inference and longer generated responses could shift the balance, and they say so plainly.

Tom: But the feasibility argument doesn't depend on those caveats. At 24 KPIs, text inputs overflow the context window, and at the edge, text-mode token counts exceed the memory ceiling of representative hardware. The visual pathway is the only route that fits.

Jane: That's the message I want listeners to keep. This paper gives us a concrete direction for energy-aware inference, and it measures the benefits instead of just asserting them.

Tom: Good note to end on. We're done with this paper, and we'll pick up the next one from the queue.

Bhavika Jalli, Nikhil Korati Prasanna, Jayanta Choudhury

Ericsson

cs.AI, cs.PF

Submitted: 2026-08-07

Updated: 2026-08-10

Comments: Accepted at the 14th European Conference on Renewable Energy Systems (ECRES), July 7--9, 2026, London, UK

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 66/100

The gist: The paper "A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy" by Bhavika Jalli, Nikhil Korati Prasanna, and Jayanta Choudhury (Ericsson),

Key concepts

Vision-Language Model (VLM): An AI model that processes both images and text. In this paper, VLMs are fed time-series data as 2D plots instead of raw numbers, allowing them to see patterns like spikes and humps directly. This reduces token count and energy use while improving accuracy compared to text-only LLMs.
Token: A unit of text that an AI model processes. Raw time-series numbers explode into tens of thousands of tokens, while images use far fewer. Since energy scales with token count, reducing tokens is key to cutting inference costs. The paper shows image-based input uses 3.6 to 10.4 times fewer tokens.
Inference Energy: The electricity consumed when an AI model makes predictions. It scales linearly with input token count. The paper measures energy using hardware counters and finds that image-based input reduces per-query energy by up to 18.3 times, despite the visual encoder's overhead, because token savings dominate.
J/F1 Metric: A combined metric that divides energy (in joules) by F1 score (a measure of accuracy). Lower is better. It shows efficiency per unit of accuracy. Pixtral's image pathway achieves 20.6 times better J/F1 than its text version, proving that visual representation is both cheaper and more accurate.

Terminology

Accuracy — 22× in this paper · explained in 1 episode
Benchmark — 12× in this paper · explained in 2 episodes
Tokenization — 10× in this paper · explained in 2 episodes
Edge Deployment — 7× in this paper · explained in 2 episodes
F1 Score — 6× in this paper · explained in 3 episodes
Fidelity — 5× in this paper · explained in 2 episodes
Vision-language models — 5× in this paper · explained in 2 episodes
Vision-Language Models (VLMs) — 4× in this paper · explained in 6 episodes
Fine-Tuning — 3× in this paper · explained in 5 episodes
Quantization — 3× in this paper · explained in 12 episodes
Benchmarking — 2× in this paper · explained in 4 episodes
Calibration — 2× in this paper · explained in 7 episodes
Energy Efficiency — 2× in this paper · explained in 3 episodes
European Conference on Renewable Energy Systems — 2× in this paper
Grounding — 2× in this paper · explained in 3 episodes
Large Language Model — 2× in this paper
Large Language Model (LLM) — 2× in this paper · explained in 3 episodes
Pruning — 2× in this paper · explained in 4 episodes
Trace — 2× in this paper · explained in 2 episodes
Alignment — 1× in this paper · explained in 1 episode
Communication Overhead — 1× in this paper · explained in 2 episodes
Core — 1× in this paper · explained in 2 episodes
Generalizability — 1× in this paper · explained in 6 episodes
LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
Ontology — 1× in this paper · explained in 2 episodes
Redundancy — 1× in this paper · explained in 2 episodes
Sparse Attention — 1× in this paper · explained in 5 episodes
Transformer — 1× in this paper · explained in 2 episodes
Transformer Models — 1× in this paper · explained in 2 episodes

Summary

The paper A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy by Bhavika Jalli, Nikhil Korati Prasanna, and Jayanta Choudhury (Ericsson), published on arXiv (arXiv:2608.07427v1) and accepted at the 14th European Conference on Renewable Energy Systems (ECRES), July 7–9, 2026, London, UK, addresses the energy inefficiency of Large Language Model (LLM) inference for numerical time-series data analysis (NTSDA).

The authors establish that "LLM inference accounts for over 90% of AI operational energy, scaling directly with input token count—a critical inefficiency for telecom network analytics and numerical time-series data analysis (NTSDA), where raw multivariate KPI windows from 4G/5G cell sites expand into thousands of floating-point tokens. They note that US AI data-center power demand is projected to grow from 4 GW in 2024 to 123 GW by 2035 (30×)... with data centers consuming 4.4% of US electricity in 2023 and global AI demand potentially reaching 21% by 2030. They cite measurement studies confirming that inference energy scales linearly with input token count, establishing token count as the primary controllable lever for energy reduction."

The token-energy coupling is particularly wasteful for numerical time-series data analysis, where a modest multivariate KPI window explodes into tens of thousands of floating-point tokens despite carrying far less information density than natural language. A key operational constraint is that "a single 8-KPI window of 381 time points generates 46,000–60,000 tokens, exceeding context windows of production models and approaching memory ceilings of edge GPUs, making text-based analysis both energy-wasteful and often physically infeasible."

The central idea is that Vision-Language Models (VLMs) eliminate this mismatch by rendering time series as 2-D plots, compressing temporal information into far fewer visual tokens. The paper provides the first direct energy comparison of LLM vs. VLM inference for time-series anomaly detection across three vision encoder architectures, with empirical measurements on public benchmarks and live telecom data demonstrating 1.8–2.5x energy savings with simultaneous accuracy improvements.

In their telecom anomaly detection pipeline, a fine-tuned VLM achieves 220.7% higher precision and 2.5x energy savings over its text-only counterpart, while outperforming LSTM and ARIMA baselines by over 144%. On public benchmarks, Pixtral-12B achieves a 20.6x improvement in J/F1 efficiency... at mean F1 = 0.82.

The methodology has three stages. First, the first stage renders 2D graphs of univariate time-series as raster images, mapping time to the horizontal axis and measurement values to the vertical axis, with both axes stripped of tick marks, numerical labels, and gridlines, following VLM4TS, to eliminate textual artifacts from the pixel space. The second stage performs patch generation and anomaly interval proposal following the VLM4TS temporal segmentation procedure, extending it beyond single-panel univariate graphs to vertically stacked subplot compositions, where each subplot renders a distinct univariate component of a multivariate time-series. The stacked-subplot formulation is geometrically motivated: a multivariate time-series of dimension d traces a trajectory on a d-dimensional manifold, and a vertically aligned stack of d univariate subplots constitutes a visual cross-section of that manifold. Third, structured prompts condition VLM inference over rendered images, encoding the detection objective alongside domain-specific morphological primitives (spike, hump, oscillatory deviation).

Energy measurement uses the Zeus framework, reading cumulative hardware energy counters via NVML (nvmlDeviceGetTotalEnergyConsumption) to avoid sampling error from power-polling approaches, with instantaneous power polled at 50 ms intervals and integrated using the trapezoidal rule. Each configuration is evaluated over 3 independent runs of 256 output tokens, preceded by a warmup pass to stabilize GPU clock frequencies.

Model selection covers three VLMs... across principal vision encoder strategies: Llama-3.2-90B-Vision (cross-attention fusion with a fixed 6,404-token visual representation regardless of resolution), Qwen2.5-VL-72B (dynamic-resolution patching, scaling the token budget with image height and number of KPI subplots), and Pixtral-12B (decomposes images into 16×16 pixel patches, yielding the highest vision token counts, while preserving spatial detail).

For the public dataset (realAWSCloudwatch, a subset of the VLM4TS benchmark), 17 candidate signals; 10 are retained per model, stratified by series length (1,243–4,730 time points), with detection performance reported as F1 at significance threshold α = 0.01. The telecom-specific experiment uses operational KPI data from a live 4G/5G telecom network, spanning 9 dates and 209 cells in April 2025, where each cell reports 24 KPIs at 15-minute intervals, 96 time points per day. For the text modality, all 8 KPI time-series were serialized as comma-separated floating-point values... and tokenized using each model's native tokenizer, while for the image modality, the same 8 KPIs were rendered as a stacked subplot chart... at 150 DPI.

Public dataset results: Image modality consistently outperforms text across all architectures (mean F1: 0.70–0.88 vs. 0.58–0.66). Energy reductions relative to text were 4.5× (Llama-3.2-90B), 13.8× (Qwen2.5-VL-72B), and 18.3× (Pixtral-12B). Table I reports per-query energies: Llama text 69,187 J vs. image 15,311 J; Qwen text 113,484 J vs. image 8,218 J; Pixtral text 39,663 J vs. image 2,166 J. In terms of J/F1 efficiency, Pixtral-12B achieves the best image efficiency at 2,538 J/F1 versus 52,356 J/F1 for text (20.6x gain), delivering mean F1 = 0.82 at the lowest absolute energy (2,166 J/query), while Qwen2.5-VL-72B text is the least efficient at 197,617 J/F1.

Telecom dataset results: Token reductions are architecture-dependent: "Llama's cross-attention fusion projects any image into a fixed 6,404-token budget (7.2× reduction), Qwen's dynamic resolution tiles images into 28×28 patches with adjacent-token merging for the largest compression at 10.4×, while Pixtral's 16×16 patch tokenization preserves spatial fidelity at a more modest 3.6×. Measured energy reductions are 2.5x on Llama (59,751 J vs. 23,557 J), 1.8x on Qwen (4,363 J vs. 2,397 J), and 2.5x on Pixtral (7,768 J vs. 3,151 J)."

Accuracy: The fine-tuned (SFT) VLM achieves precision = 0.465, a 220.7% improvement over the text-only LLM (0.145), while consuming 7.2x fewer tokens, with F1 = 0.464 vs. 0.185 for text-only LLM. The zero-shot VLM already outperforms all text-based methods (F1 = 0.360 vs. 0.185–0.190), confirming the visual representation itself provides a fundamental advantage.

Token scaling: At 24 KPIs, all three text representations exceed 128K tokens... surpassing the context window of most production deployments and forcing lossy truncation that the visual modality avoids entirely. Vision token growth with KPI count is architecture-dependent: Qwen's dynamic tiling scales most aggressively with image height, while Llama's fixed-budget encoding remains constant regardless of subplot count.

Image compression: Reducing resolution from 150 DPI to 75 DPI cuts vision tokens by 70% (17,303 to 5,112) and inference energy by 24% (8,818 J to 6,660 J) with no degradation in detection accuracy (F1 = 0.354 vs. 0.347). Even at 50 DPI... F1 remains within 2% of the baseline (0.340 vs. 0.347). JPEG compression at 75 DPI produces identical token counts to PNG at the same resolution, with negligible impact on accuracy (F1 = 0.351).

A telecom operator monitoring 209 cells at 15-minute intervals generates ∼20,064 queries/day. The VLM modality saves ∼7.2 MJ/day, equivalent to the daily residential energy consumption of 2.4 average US households. The authors note that the VLM token reduction is not merely an efficiency gain but a deployment prerequisite, as text-mode token counts exceed the demonstrated memory ceiling of representative edge hardware.

Architecture guidance: The choice depends on whether the deployment prioritizes energy predictability (Llama), maximum compression (Qwen), or spatial fidelity (Pixtral).

Limitations acknowledged include single-GPU configurations only, only three VLM architectures evaluated, the fixed 256 output token constraint, and single-operator telecom evaluation. Future work should address full-system energy accounting, adaptive resolution strategies based on signal complexity, streaming inference with energy-aware batching, and carbon intensity calibration for sustainability reporting.

The paper concludes: "Our evaluation confirms that VLMs reduce inference energy for numerical time-series workloads while improving detection accuracy. Across three vision encoder architectures, the image modality achieves 3.6–10.4x token reduction and 1.8–2.5x energy savings, with Pixtral-12B delivering 20.6x improvement in energy-normalized efficiency (2,538 vs. 52,356 J/F1)."

Improvements for AI systems

Improvements to AI systems:

Use vision-language models (VLMs) instead of text-only LLMs for all numerical time-series analysis. Render multivariate time-series as vertically stacked subplot images (axes stripped of labels/gridlines) and feed those to a VLM. This reduces token count by 3.6–10.4×, cuts inference energy by 1.8–2.5×, and simultaneously improves anomaly-detection F1 (e.g., telecom precision +220.7%, F1 from 0.185 to 0.464).
Adopt Pixtral-style 16×16 patch tokenization for maximum energy-normalized accuracy. This architecture achieves 20.6× better J/F1 efficiency (2,538 J/F1 vs. 52,356 J/F1) at mean F1 = 0.82, making it the best default choice for cost-constrained deployments.
For deployments requiring predictable energy budgets, use Llama-style cross-attention vision encoders with a fixed 6,404-token visual representation, independent of image resolution or KPI count—ensuring constant latency and energy per query regardless of input complexity.
For maximum compression in memory-limited edge environments, use Qwen-style dynamic-resolution tiling with adjacent-token merging (10.4× token reduction), enabling analysis of 24+ KPI windows that would otherwise exceed 128K tokens and force lossy truncation in text mode.
Implement adaptive image-resolution control. Dynamically downsample from 150 DPI to 75 DPI—reducing vision tokens by 70% and inference energy by 24% with no accuracy drop (F1 0.354 vs. 0.347). Use JPEG compression at that resolution to further reduce storage/bandwidth without harming performance.
Eliminate textual artifacts from the pixel space by removing tick marks, numerical labels, and gridlines from rendered time-series plots, forcing the VLM to learn purely from visual morphology rather than read embedded text.
Encode detection objectives with structured prompts that include domain-specific morphological primitives (e.g., spike, hump, oscillatory deviation) to condition the VLM's visual reasoning and improve anomaly localization.
Integrate energy accounting into the inference stack. Use cumulative hardware energy counters (NVML/Zeus) and warmup-stabilized GPU clocks to measure and optimize per-query energy as a first-class metric alongside accuracy.

What the improved AI system can do:

Perform real-time anomaly detection on live telecom 4G/5G KPI streams (e.g., 209 cells, 15-minute intervals, 20,064 queries/day) while saving 7.2 MJ/day—equivalent to the daily residential energy use of 2.4 US households—and still outperform LSTM, ARIMA, and fine-tuned text-only LLMs by over 144% in F1.
Analyze multi-KPI windows (up to 24+ KPIs) that would exceed the context windows of most production text-based LLMs, avoiding lossy truncation and enabling full-fidelity edge deployment on GPUs with limited memory.
Maintain high detection accuracy (F1 within 2% of baseline) even when input images are aggressively compressed to 50 DPI or JPEG, allowing operators to trade visual quality for speed and energy on the fly.
Provide architecture-aware deployment choices: predictable energy (Llama), maximum compression (Qwen), or best accuracy-per-joule (Pixtral)—so system operators can match model selection to their hardware, carbon-intensity, and latency constraints.

Abstract

LLM inference accounts for over 90% of AI operational energy, scaling directly with input token count---a critical inefficiency for telecom network analytics and numerical time-series data analysis (NTSDA), where raw multivariate KPI windows from 4G/5G cell sites expand into thousands of floating-point tokens. Vision-Language Models (VLMs) eliminate this mismatch by encoding time-series as 2D plots, achieving 3.6-10.4x input token reduction across Llama-3.2-90B, Qwen2.5-VL-72B, and Pixtral-12B architectures. This translates to 1.8-2.5x measured inference energy reduction, saving approximately 7.2 MJ/day at telecom edge deployments and CloudRAN that monitor 200 cells per 15-minute interval. Critically, efficiency gains do not sacrifice accuracy: a fine-tuned Llama-3.2-90B-Vision VLM achieves 220.7% higher precision than its text-only counterpart and outperforms LSTM and ARIMA baselines by over 144% on telecom anomaly detection. On public benchmarks, Pixtral-12B achieves a 20.6x improvement in J/F1 score at mean F1 = 0.82. At 24 KPIs, text representations exceed the 128K context window of most production LLMs, rendering text-only processing infeasible without truncation, while visual representations remain within standard limits. These results establish VLMs as an energy-efficient and accuracy-superior modality for numerical time-series workloads, providing empirical grounding for AI inference systems that treat energy consumption as a first-class engineering constraint.