Deploying 2:4 Sparsity with FP8 on Hopper: A Production Cookbook

On Hopper‑class GPUs, the combination of 2:4 structured sparsity and FP8 precision routinely delivers 1.5–2.0× end‑to‑end throughput gains for LLM decoding, while cutting energy per token by 20–40% when implemented in production engines like TensorRT‑LLM. The kernel‑level picture is simple: NVIDIA’s Sparse Tensor Cores double effective math throughput for eligible GEMMs under the 2:4 N:M pattern, and Transformer Engine’s FP8 pipeline reduces bandwidth and scales activations robustly. But making those gains stick in production—without tanking quality—requires a careful pipeline: the right layers, properly curated calibration, safe pruning, quick adapter‑based recovery, and a well‑tuned engine build.

This article is a hands‑on playbook to take a dense model to a pruned, adapter‑recovered, FP8‑calibrated build in TensorRT‑LLM. We’ll show you how to baseline and profile, select layers and formats compatible with 2:4 pipelines, apply structured pruning across linear/FFN paths, recover with LoRA/AdaLoRA, integrate into a production engine with static shapes and fused attention, and validate with a robust matrix of metrics, latency distributions, memory, and power logging. By the end, you’ll have a repeatable, rollback‑safe process you can operate in a real fleet.

Architecture/Implementation Details

End‑to‑end pipeline overview

1. Baseline in production conditions

• Build a dense FP16 or FP8 baseline in TensorRT‑LLM with your production decoding params (batching, max prompt/response lengths, tokenizer, scheduler).
• Enable fused attention (e.g., FlashAttention‑style kernels) so the MLP matmuls become the dominant compute path you’ll later sparsify.
• Measure tokens/s, p50/p99 latency, peak/activation memory, and GPU power (nvidia‑smi dmon or NVML). This is your control.

2. Choose 2:4‑eligible layers and orientation

• Target linear/FFN mats with large K dimensions: attention QKV/proj and MLP up/down projections are typically eligible.
• Avoid embedding tables, layer norms, and the final LM head for pruning.
• Respect the 2:4 grouping along the innermost compute dimension expected by cuSPARSELt (groups of 4 contiguous values along K) so Sparse Tensor Cores can engage.

3. Calibrate for FP8 and sparsity

• Curate a calibration set of 2–5k prompts that mirrors production: instruction, reasoning, and code. Include a long‑context slice.
• Run FP8 activation/weight scaling with Transformer Engine (per‑tensor dynamic scaling) to avoid overflow while you collect activation stats.

4. Apply 2:4 structured pruning safely

• Use magnitude or activation‑aware scores within each 4‑value group to drop the two least‑important elements.
• Start at 30–40% global sparsity (2:4 is 50% within eligible mats but applied only to selected layers). Avoid aggressive pruning in late attention layers which can be KV‑critical for long‑range behavior.

5. Adapter‑based recovery (LoRA/AdaLoRA)

• Attach low‑rank adapters to pruned modules and fine‑tune for a few thousand steps on a task mixture similar to your calibration set.
• Use early stopping based on a small validation basket (e.g., slices of MMLU/GSM8K/MT‑Bench/HumanEval) to recover 0.5–2 points without expensive full SFT.

6. Precision pipeline and post‑pruning recalibration

• Establish a stable FP8 baseline first, then prune, then re‑calibrate FP8 scales post‑pruning.
• If you prefer INT8 (W8A8 or weight‑only), create a pre‑pruning baseline (LLM.int8(), GPTQ), prune, then re‑calibrate and validate.

7. Engine integration for production

• Export pruned weights with 2:4 masks.
• Build a TensorRT‑LLM engine with FP8 and sparsity enabled, static shape profiles covering your prompt/response envelope, and fused attention enabled.
• Verify sparse kernel engagement across all configured shapes; shape mismatches will silently disable sparsity.

8. Validate and iterate

• Re‑measure tokens/s, p50/p99 latency, memory, and power. Expect 1.3–1.8× from 2:4 alone and 1.5–2.0× with FP8 on H100/H200, with 20–40% energy per token reductions.
• Evaluate MMLU, GSM8K, HumanEval, MT‑Bench, BBH, and a long‑context task with fixed decoding params for apples‑to‑apples comparisons.

Diagram (conceptual): Data flows from tokenizer → FP8‑calibrated embeddings → attention blocks (FlashAttention‑2 fused kernels) → FFN blocks (2:4 sparse matmuls via cuSPARSELt) → LM head. Recovery adapters sit on pruned linear paths. Static shape profiles ensure kernels stay on optimized paths.

Practical performance notes on Hopper

• Eligibility is binary: only correctly masked 2:4 mats get the 2× kernel uplift. A single mis‑grouped axis or unsupported layout drops you to dense kernels.
• FP8’s biggest gains come from bandwidth savings and keeping mats in fast paths; keep scale outliers in check with updated calibration after pruning.
• Fused/optimized attention (e.g., FlashAttention‑2) reduces attention overhead, amplifying the speedup realized from sparsifying the MLPs.

Comparison Tables

Precision and sparsity recipes on Hopper (TensorRT‑LLM)

Recipe	Expected e2e speedup (decoding)	Memory/BW impact	Risk profile	Notes
Dense FP16	1.0×	High	Low	Baseline; easiest to validate
Dense FP8	1.2–1.4×	Activation BW ↓	Low–mod	Requires careful scaling; good first step
2:4 + FP16	1.3–1.8×	Weight BW ↓	Mod	Ensure layer eligibility/orientation
2:4 + FP8	1.5–2.0×	Weight + activation BW ↓	Mod	Sweet spot on H100/H200
INT8 W8A8	1.2–1.6×	Weight/act BW ↓	Mod	Broadly supported; recalibrate after pruning

What to prune (dense LLMs)

Module	2:4 eligible	Risk to quality	Guidance
Attention Q/K/V	Yes	Medium	Prune conservatively in later layers; preserve KV‑critical heads
Attention output proj	Yes	Low–mod	Generally safe; validate long‑context
FFN up/down (gate)	Yes	Low	Primary target for 2:4 speedups
Embeddings	No	High	Do not prune
RMSNorm/LayerNorm	No	High	Do not prune
LM head	Generally avoid	High	Optional only with strong validation

Pros and cons (2:4 vs alternatives on NVIDIA)

Approach	Pros	Cons
2:4 N:M	Kernel‑level 2× math throughput; production‑ready in TensorRT‑LLM	Pattern constraints; strict layout/orientation
FP8 only	Easy to adopt; portable across layers	Smaller gains; still bandwidth‑bound in places
Unstructured sparsity	High compressibility	Little/no speedup without specialized kernels
Block‑structured sparsity	Good locality; easier kernels than unstructured	Requires tuned custom kernels or specific coverage

Best Practices

Calibration set curation and scaling hygiene

• Mix instruction, reasoning (math), code, and long‑context prompts. 2–5k prompts is enough for stable scaling and pruning scores.
• Fix decoding params (temperature/top‑p/max‑new‑tokens) for both calibration and evaluation to avoid confounds.
• For FP8, use Transformer Engine’s per‑tensor dynamic scaling and re‑run calibration after pruning to account for distribution shifts.

Safe structured pruning

• Prune only layers with proven 2:4 support in your engine. Follow cuSPARSELt’s grouping along the inner K dimension and keep weights in supported layouts.
• Stage sparsity increases (e.g., 20% → 30% → 40%) with quick evaluations between steps. Stop when MMLU/GSM8K move by ~1–2 points.
• Treat late attention layers as KV‑critical: prune less aggressively there, and preserve heads known to carry long‑range signal.

Adapter‑based recovery

• Start with LoRA rank 8–16 on attention and FFN paths; raise rank only if validation does not recover within 1 point on your metrics.
• AdaLoRA can allocate rank dynamically across modules; useful when pruning budgets differ by layer depth.
• Train with your calibration mix and a light weight on code/math to stabilize GSM8K/HumanEval.

Engine integration and shape discipline

• Build engines with static shapes that cover real workloads (prompt/response buckets). If you fall off a profiled shape at runtime, sparsity may be disabled.
• Enable fused attention (FlashAttention‑2 style) to surface the MLP bottleneck and maximize end‑to‑end gains.
• Verify sparsity engagement by inspecting kernel traces and TensorRT‑LLM logs; a sudden p99 spike often signals a fallback to dense paths.

Validation matrix and guardrails

• Report: tokens/s, p50/p90/p99 latency, peak/activation memory, energy per token (via power logging). Compare apples‑to‑apples.
• Task suite: MMLU, GSM8K, HumanEval, MT‑Bench/BBH, plus at least one long‑context benchmark.
• Canary tests: short prompts targeting safety, instruction‑following, and long‑context integrity.
• Rollback plan: versioned artifacts per stage (baseline → pruned → recovered → recalibrated). Progressive traffic ramp with automated rollback on error budgets or p99 regressions. ✅

Troubleshooting quick hits

• FP8 overflow/NaNs: tighten clip‑max or re‑collect scales with outlier‑heavy prompts.
• Speedup <1.2×: check that 2:4 masks align to the correct axis and that all production shapes are profiled with sparsity enabled.
• Attention regressions: roll back Q/K pruning in late layers or increase LoRA rank on those modules.
• p99 spikes: ensure the scheduler doesn’t exceed profiled max lengths, and that batching does not introduce shape drift.

Practical Examples

1) 2:4 mask application (PyTorch, illustrative)

import torch

@torch.no_grad()
def apply_2_4_mask(weight: torch.Tensor, group_dim: int = -1):
 # Reshape so groups of 4 lie along the innermost dimension
 assert weight.shape[group_dim] % 4 == 0
 w = weight.transpose(group_dim, -1)
 g = w.reshape(*w.shape[:-1], w.shape[-1] // 4, 4)
 # Magnitude score within each group of 4
 scores = g.abs()
 # Keep top‑2 per group
 top2 = scores.topk(k=2, dim=-1).indices
 mask = torch.zeros_like(g, dtype=torch.bool)
 mask.scatter_(-1, top2, True)
 w_pruned = (g * mask).reshape_as(w)
 return w_pruned.transpose(group_dim, -1)

# Example: apply to an MLP up‑projection
for name, mod in model.named_modules():
 if isinstance(mod, torch.nn.Linear) and is_eligible(name):
 mod.weight.copy_(apply_2_4_mask(mod.weight, group_dim=1)) # group along K

Note: The exact grouping/layout must match cuSPARSELt’s expectations for Sparse Tensor Core engagement.

2) FP8 calibration with Transformer Engine (simplified)

import transformer_engine.pytorch as te

class FP8Block(te.fp8_autocast):
 def __init__(self, enabled=True):
 super().__init__(enabled=enabled)

# Calibration pass
with te.fp8_autocast(enabled=True):
 for batch in calib_loader:
 _ = model(**batch)
# Save collected scales (framework manages per‑tensor stats)

Reference: NVIDIA Transformer Engine provides FP8 casting, scaling recipes, and integration guidance.

3) LoRA recovery (PEFT‑style pseudocode)

from peft import LoraConfig, get_peft_model

lora = LoraConfig(r=16, lora_alpha=32, target_modules=["q_proj","k_proj","v_proj","o_proj","up_proj","down_proj"], lora_dropout=0.05)
model = get_peft_model(model, lora)
# Train for 3–10k steps on mixed instruction/math/code; early stop on MMLU/GSM8K slice

Background: LoRA/AdaLoRA recover quality at low compute following pruning.

4) TensorRT‑LLM build (example config excerpt)

{
 "precision": "fp8",
 "enable_sparse_weights": true,
 "fused_attention": "flash_v2",
 "profiles": [
 {"prompt": [1, 2048], "response": [1, 512]},
 {"prompt": [1, 4096], "response": [1, 1024]}
 ],
 "plugins": {"kv_cache": {"static": true}}
}

Build and run (CLI varies by version; check TensorRT‑LLM docs):

trtllm-build --model./pruned_lora_recovered --config./trt_config.json --output./engine
trtllm-run --engine./engine --dataset./eval.jsonl --metrics tokens_per_s,latency_p50,latency_p99

Documentation: TensorRT‑LLM repository and docs for enabling FP8, sparsity, and fused kernels.

5) Power and latency logging

# Power
nvidia-smi dmon -s pucmt -i 0 -o DT >> power.log &
# Latency distribution via your runner
trtllm-run... --metrics latency_histogram

6) INT8 alternative (weight‑only)

If your stack prefers INT8, establish a GPTQ/LLM.int8() baseline, prune, then re‑quantize/re‑calibrate before engine build.

Conclusion

2:4 sparsity plus FP8 on Hopper is no longer a lab trick—it’s a deployable recipe that consistently yields 1.5–2.0× throughput and material energy savings when executed with discipline in TensorRT‑LLM. The critical path is operational: nail your baseline, prune only eligible layers with correct orientation, recalibrate precision after structural changes, recover with lightweight adapters, and keep engines on optimized kernels with static shape profiles. A rigorous validation matrix, canaries, and rollback guardrails turn those gains into something you can trust at scale.

Key takeaways

• Establish a dense FP16/FP8 baseline in TensorRT‑LLM with fused attention before any pruning.
• Apply 2:4 only to eligible linear/FFN mats, respecting cuSPARSELt’s grouping; prune conservatively in late attention layers.
• Re‑calibrate FP8 (or INT8) after pruning and run brief LoRA/AdaLoRA recovery to keep metric deltas within ~1–2 points.
• Validate tokens/s, latency p50/p99, memory, and power; use MMLU, GSM8K, HumanEval, MT‑Bench/BBH, and long‑context to catch regressions.
• Guard with canaries and versioned rollbacks; shape discipline is essential to keep sparse kernels engaged.

Next steps

• Prototype the pipeline on a mid‑size model (e.g., 7–13B) with a small calibration set to validate tooling.
• Move to your target scale, stage sparsity in increments, and codify pass/fail thresholds for quality and latency.
• Automate engine builds for each profile bucket and integrate power/latency telemetry into your deploy pipeline.

Forward‑looking: as TensorRT‑LLM coverage widens and FP8 tooling matures, expect easier, more automated paths to mix 2:4 sparsity with precision scaling—and more of your fleet running comfortably in the fast lane.

Sources & References