Cut GPU Costs in Half: Buzz HPC's Memory Hack for 370B Parameter Models

Why This Matters

GPU memory, not FLOPs, is the hard ceiling on how large an LLM you can load and how long a context you can serve. BF16 models use 16 bits per weight; that doubles the footprint relative to INT8 quants but preserves training‑time fidelity. DFloat 11 (DF11) compresses BF16 losslessly to ≈11 bits by Huffman‑coding the sparse exponent field.
Result: ~30 % smaller footprints at runtime, 100 % identical outputs.

Headline Gains on Real Silicon

KV‑cache wins. Because DF11 compresses activations too, every token’s KV entries shrink by 30 %. On long‑context workloads (chat history, RAG, ERP docs) that translates to +43 % context length before evicting tokens.

Performance in Practice

Throughput: At batch ≥ 32, DF11 decoding is amortised; on an A100 40 GB the team measured only 2 % slower than BF16.
Latency edge cases: Batch = 1 sees ~40 % hit—use BF16 when you already fit and care only about single‑prompt ping.
Beats CPU offload by ×38 when the alternative is spilling to RAM.

How to Use DF11 on Buzz

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# one‑liner on a Buzz A6000 48 GB instance
pip install dfloat11 vllm==0.4.1

HF_MODEL="leanmodels/df11_llama-3.1-8b-it"
python -m vllm.entrypoints.openai.api_server \
       --model $HF_MODEL \
       --dtype df11  \
       --gpu-memory-utilization 0.9

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Hugging Face: ready‑made DF11 checkpoints for Llama‑3.x, Qwen 2.x, Mistral, etc.
DIY: dfloat11.compress.py your_model_dir produces a DF11 → .pt compatible with vLLM & TensorRT‑LLM.
Mixed fleets: Buzz’s scheduler lets you pack DF11 shards across heterogeneous GPUs; DF11 stays bit‑exact so cross‑device results stay deterministic.

Cost Impact

Running Llama‑3‑70B‑Instruct on a single H100‑80 GB with DF11 versus dual H100s with BF16:

Annualised, that’s >$47k savings per replica before power rebates.

When Not to Use DF11

You’re happy with lossy W8A8 quantisation and have already validated quality.
Latency‑critical, batch‑1 endpoints that already fit in VRAM.
Training or full‑gradient finetuning (exponent bits do change).

Efficiency Begets Appetite with Jevons Paradox

In 1865, economist William Stanley Jevons observed that technical efficiencies tend to increase overall consumption of a resource—because lower cost unlocks new use‑cases. Buzz already sees this dynamic with DF11 pilots:

Teams that could only afford 7‑B‑parameter assistants last quarter are now spinning up 34‑B chatbots—and need four‑times the context length.
RAG integrators who squeezed inference into one GPU per tenant now launch burst pools of 20–30 instances to handle full‑document re‑ranking.

Takeaway: DF11 slashes unit cost, but aggregate demand will likely outpace the savings. Buzz’s upcoming datacenter expansion—plus fresh H100/H200 inventory—ensures capacity keeps pace with the Jevons‑curve uptick.

Design for scale: treat your DF11 migration as step 1. Step 2 is autoscaling policy, placement groups, and inter‑GPU speed (NVLink vs PCIe) so you can ride the demand wave without bottlenecks.