Question the measurement
My LLM judge was flipping a coin a third of the time
What happened when I tested the judge I had trusted for months.
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Small thoughts, things I learned the hard way, and ideas that needed more room.
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Every FP8 GEMM traps on DGX Spark (SM121)
An exact failure, the hardware behind it, and the two-line fix.
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8 pieces about Inference & performance
Field note
Dropping --kv-unified silently turns a 262144-token server into a 65536-token one
llama-server divides the context size by the slot count unless --kv-unified is set: n_ctx_seq = n_ctx / n_seq_max. There is no error and no warning. The same command line that asks for --ctx-size 262144 --parallel 4 serves 65536 per sequence, and the only evidence is one line in the boot log. Long prompts then fail or shift in ways that look like a model problem rather than a configuration one.
Field note
GGML_ASSERT(layer.nextn.hc_head_norm) — two MTP pull requests disagree about one tensor name
A published Qwen3.8-Flash-Next MTP head loads against PR #27739 and aborts against PR #27836 with "MTP block missing nextn.hc_head_norm". The head is not damaged and no tensor is missing: the same three hyper-connection tensors are exported as top-level output_hc_norm/down/up by one PR and expected as blk.N.nextn.hc_head_norm/down/up by the other, because a standalone head file has no trunk to collide with and a grafted head does. The published merge script filters to blk.* and silently drops all three. Rename them instead of dropping them.
Field note
A per-request speculative.type of "none" still drafted 198 tokens
llama-server accepts per-request speculative.n_max, p_min and type, which makes sweeping a drafter on one load look safe. It is not: the per-request value appends to the launch-time list rather than replacing it, so the echoed settings read speculative.types = none,draft-mtp and every row of a six-configuration sweep was the same configuration. The "off" arm reported 198 drafted tokens. A speculative-decoding baseline needs a server restart, and the echoed settings block is the only thing that reveals it.
Field note
A quantized KV cache crashes qwen4exp on llama.cpp, in two different ways
A quantized KV cache (--cache-type-k q8_0) crashes qwen4exp twice, not once. The first crash is a Hadamard-rotation gate that qwen4exp does not implement; disabling that rotation produces a server that loads, saves 4 GiB, and matches f16 on every quality test run against it, then fails a second, different assert once real concurrent load arrives across all four slots. That second assert looked quantization-specific at first. It is not: the identical assert was later confirmed on f16 too, with no quantization involved, so the real cause is a multi-slot desync in llama_memory_hybrid_idx that q8_0 merely reaches sooner. Verdict: f16, run with --parallel 1 until upstream fixes the desync.
Field note
The Qwen3.8-Flash-Next MTP head was missing from the August 26 GGUFs
On August 26, the converter disabled MTP export and the architecture lacked its inference path. This historical field note now links to the August 28 follow-up, which documents working speculative decoding using a third-party head, a draft PR, and a local GGUF graft.
Field note
Which NVFP4 MoE backend actually works on SM121 (it depends on the model)
One model needs --moe-backend cutlass and is slow under marlin. Another needs VLLM_SCALED_MM_BACKEND=marlin and crashes without it. NVIDIA officially recommends marlin. All three statements are true.
Field note
FlashInfer CUTLASS MoE fails TMA descriptor init on SM121
FlashInfer ships a precompiled fused-MoE library built for SM120. Its TMA descriptors do not initialize on SM121, so concurrent MoE batches crash after hours of clean operation. vLLM cannot patch it.
Field note
Every FP8 GEMM traps on DGX Spark (SM121)
vLLM guards its FP8 CUTLASS kernels on __CUDA_ARCH__ == 1200 and executes a deliberate trap instruction on anything else. DGX Spark is arch 1210, so every FP8 GEMM crashes. Two lines fix it.