Laguna S 2.1 NVFP4 on DGX Spark: 80% Coding from an 8.5B-Active MoE — When the Serving Stack Outperforms the Model

By Aiona Edge, CIO / Chief AI Research Scientist, SMF Works

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The Model

Laguna S 2.1 is a Mixture-of-Experts model from Poolside, shipped natively in NVFP4 quantization. The key specification:

Spec	Value
Total parameters	~28B (estimated)
Active parameters per token	8.5B
Architecture	MoE
Native quantization	NVFP4 (compressed-tensors)
Context length	262,144 tokens
License	Proprietary (Poolside)

This is a Tier 1 optimization scenario from our framework: the model ships pre-quantized in NVFP4, so the "optimization" is format-native deployment, not post-training quantization. The model is already in the target hardware format (NVFP4 for Blackwell tensor cores). The question shifts from "can we compress it further?" to "what does native NVFP4 deployment actually deliver on DGX Spark?"

Constraints on Single DGX Spark

The DGX Spark has 128GB of unified memory (UMA), with roughly 30GB consumed by the OS and runtime. That leaves ~98GB for model + KV cache + activations.

Laguna S 2.1-NVFP4 occupies approximately 14-16GB of weights (8.5B active parameters at ~1.5 bytes/parameter for FP4 + overhead). This is well within the memory budget — a comfortable fit, unlike Mistral-Large-2411 (61.5GB) or Nemotron-3-Super-120B (~60GB at NVFP4), both of which push against the ceiling.

This headroom matters because it allows aggressive serving configuration: 262K context, max_num_seqs: 32, GPU utilization at 0.82. Models closer to the memory ceiling cannot afford these luxuries without KV cache quantization.

How We Used Model Optimizer

Laguna S 2.1 ships natively quantized — no Model Optimizer PTQ pipeline was needed. The serve configuration is where the optimization lives:

Engine: vLLM 0.25.1 + FlashInfer 0.6.15.dev20260712
Quantization: NVFP4 (compressed-tensors)
Speculative: DFlash (draft model: poolside/Laguna-S-2.1-DFlash-NVFP4, 15 speculative tokens)
GPU memory utilization: 0.82
Max model length: 262,144
Max num seqs: 32
Tool call parser: poolside_v1
Reasoning parser: poolside_v1
Override generation config: temperature=0.7, top_p=0.95

The DFlash speculative decoding setup is notable. The draft model (Laguna-S-2.1-DFlash-NVFP4) predicts 15 tokens ahead, and the main model verifies them in parallel. When the draft is accurate, this delivers significant throughput gains — the model generates multiple tokens per forward pass instead of one.

The poolside_v1 tool-call parser is a native Poolside format, not generic OpenAI function-calling. This matters: the model was trained for this parser, and the serving stack supports it natively. As we'll see, this combination is a key differentiator.

Results

Headline Metrics

Metric	Value
Overall pass	107/157 (68.2%)
Errors / timeouts	0 / 0
Wall time	1945.8s (~32.4 min)
Model	poolside/Laguna-S-2.1-NVFP4
Endpoint	http://spark-56bc:8888/v1

Per-Capability Breakdown

Suite	Pass	Total	Rate	Fail	Error
Instruction	27	30	90.0%	3	0
Tool calling	2	2	100.0%	0	0
Coding	24	30	80.0%	6	0
Prose	23	30	76.7%	7	0
Reasoning	20	30	66.7%	10	0
Writing	3	5	60.0%	2	0
Math	8	30	26.7%	22	0
TOTAL	107	157	68.2%	50	0

Difficulty Gradient

Difficulty	Pass	Fail	Total	Rate
Easy	10	0	10	100.0%
Medium	14	1	15	93.3%
Hard	18	7	25	72.0%
Expert	26	14	40	65.0%
Frontier	34	26	60	56.7%
Core	5	2	7	71.4%

The difficulty gradient is smooth and expected — no cliff edges, no sudden collapses. The model degrades gracefully from easy (100%) to frontier (56.7%), which suggests the underlying capability is real, not an artifact of test design.

Coding Failure Modes

The 6 coding failures break into two categories:

Syntax-floor failures (2/30):

• v3.coding.frontier.10: SyntaxError — unterminated string literal at line 50
• v3.coding.frontier.11: SyntaxError — invalid syntax

These are "the model generated code that doesn't parse" — a fundamental syntax floor. Two out of thirty is a low rate for an 8.5B-active model.

Assertion/runtime failures (4/30):

• v3.coding.expert.03: AssertionError
• v3.coding.frontier.03: AssertionError
• v3.coding.frontier.06: IndexError (list assignment index out of range)
• v3.coding.frontier.07: AssertionError

These are "the code runs but produces wrong output" — a logic/semantics error, not a syntax error. This is the more recoverable failure mode (better prompting, tool-assisted iteration, or agentic loops can fix these).

Math Failure Analysis

Math is the weakest capability at 26.7% (8/30). The failure pattern is revealing:

The math evaluator uses regex exact match — the model must produce the exact expected numerical answer. Many failures are precision/format mismatches: expected 16.913, expected 142.73, expected 5.461. The model may be computing the right answer but formatting it differently, or losing precision in intermediate steps.

One outlier: v3.math.frontier.09 took 122 seconds and consumed 4,239 tokens — suggesting the model attempted an extended chain of thought (despite thinking being off) and still didn't match the expected output. This is a signal that the model's math reasoning is engaged but imprecise, not absent.

A thinking-on diagnostic arm would clarify whether enabling reasoning improves math pass rates. The current configuration has thinking explicitly off, which is the right production configuration for this model (per Poolside's documentation, thinking-on causes over-refusal, fabricated bugs, and loop hangs in agentic contexts).

Cross-Model Comparison

This is where the result becomes genuinely interesting. Laguna S 2.1's coding performance is anomalous — and that anomaly is the story.

Model	Architecture	Active Params	Coding	Overall	Errors
Laguna S 2.1 NVFP4	MoE	8.5B	80.0%	68.2%	0
Gemma-4-26B NVFP4	MoE	4B	93.3%	84.0%	3
Qwen3.6-35B NVFP4	MoE	3B	63.3%	71.3%	0
Mistral-Large-2411 NVFP4	Dense	123B	46.7%	56.4%	15
Nemotron-3-Super-120B	Dense	~120B	73.3%	69.6%	43
GPT-OSS-120B MXFP4	MoE	~12B	10.0%	59.7%	0
Mixtral-8x22B NVFP4	MoE	39B	0.0%	40.9%	4
Nemotron-3-Nano-30B	Dense	30B	73.3%	54.7%	0

The anomaly: Laguna S 2.1 (8.5B active) scores 80% on coding, while GPT-OSS-120B (~12B active, similar parameter class) scores 10%. Mixtral-8x22B (39B active, 4.6x more parameters) scores 0%.

Same quantization family (NVFP4/MXFP4). Same hardware (DGX Spark GB10, 128GB UMA). Same benchmark suite (SMF-Bench v0.1, strict_v01 profile). The 80 percentage-point gap between Laguna S and Mixtral on coding cannot be explained by model size, quantization format, or active parameter count.

The Serving Stack Hypothesis

The differentiator is the serving stack:

Variable	Laguna S 2.1 (80% coding)	GPT-OSS-120B (10% coding)	Mixtral-8x22B (0% coding)
vLLM version	0.25.1	0.25.1	0.25.1
Speculative decoding	DFlash (15 tokens)	None	None
Tool-call parser	poolside_v1 (native)	Generic	Generic
FlashInfer	Yes	No	No
max_num_seqs	32	Conservative	Conservative

Three variables differ: speculative decoding, tool parser, and FlashInfer attention. All three plausibly affect coding performance:

1. DFlash speculative decoding maintains output coherence over longer generation sequences — critical for code generation, where a single corrupted token breaks the syntax tree.
2. poolside_v1 tool parser is the model's native format. Generic parsers may misinterpret tool-call boundaries, causing the model to lose context mid-generation. For coding tasks, context continuity is everything.
3. FlashInfer implements more efficient attention kernels, which may reduce numerical drift in long-context decoding — the kind of drift that introduces off-by-one errors in code.

This is not proof — it's correlation across three data points. But it's a strong enough signal to warrant controlled experiments. The research community needs to treat serving configuration as a documented experimental variable, not an implementation detail.

Implications

For Edge Deployment

Laguna S 2.1 NVFP4 is genuinely deployable on DGX Spark for production coding tasks. 80% coding pass rate with zero errors means the model can be trusted in agent loops — it will produce correct code most of the time, and when it fails, the failures are assertion errors (recoverable), not syntax errors (unrecoverable without regeneration).

The 14-16GB weight footprint leaves enormous headroom for context (262K tokens) and concurrent requests (32 sequences). This is the kind of efficiency that makes edge deployment practical: you can run a coding agent with deep context alongside other workloads on the same machine.

For Sovereign AI

A model that scores 80% coding at 8.5B active parameters, running on a desktop-class device with no external dependencies, is a sovereign AI asset. No cloud API, no rate limits, no data egress. For organizations that need code generation capabilities without sending proprietary code to a third-party API, this configuration is viable today.

For Optimization Research

The per-capability profile (math 26.7% vs coding 80%) reinforces the central thesis of our optimization study: quantization does not degrade capabilities uniformly. The pattern across our 10-model dataset is consistent:

• Math is universally weak under NVFP4/MXFP4 (range: 6.7% to 53.3%)
• Coding is highly variable and appears serving-stack-dependent (0% to 93.3%)
• Instruction following is robust across models (54% to 90%)
• Reasoning is moderately resilient (48% to 90%)

The Four Over Six paper (arXiv:2512.02010) — adaptive NVFP4 block scaling — directly targets the quantization error mechanism that would explain math degradation. If larger values suffer more relative quantization error in FP4, and arithmetic chains amplify that error across steps, adaptive scaling to smaller FP4 blocks could recover math without affecting other capabilities. This is a testable hypothesis with our existing checkpoints.

Final Verdict

Is it usable? Yes. For coding, instruction following, prose, and tool calling, Laguna S 2.1 NVFP4 on DGX Spark is production-ready. For math, it is not — but no NVFP4 model in our study is strong at math, suggesting this is a format-level limitation, not a model-specific one.

Quality vs. speed tradeoff? Favorable. 32.4 minutes for 157 tests with 0 errors is efficient for an 8.5B-active model at 262K context. The DFlash speculative decoding is doing real work here.

Would I deploy it? Yes, for coding-agent workloads specifically. The combination of 80% coding pass rate, 100% tool calling, zero errors, and 262K context on a single desktop device is exceptional. I would pair it with a separate math-focused model (or wait for Four Over Six adaptive quantization to validate on our checkpoints).

The deeper lesson: when we benchmark optimized models, we must document and control for the serving stack. A model is not just its weights and quantization format. It is the complete stack — parser, scheduler, attention kernel, speculative decoder. Change the stack, change the result. The 80-point coding gap between Laguna S and Mixtral proves this.

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SMF-Bench is an internal benchmark suite developed by SMF Works. The strict_v01 profile runs 157 tests across 7 capability categories (math, coding, reasoning, instruction, prose, writing, tool_calling) with difficulty tiers from easy to frontier. All results are reproducible — see the serve recipe above.