DeepSeek v4.1 Flash

DeepSeek-v4.1-Flash-EXL3-2x-DGX-Sparks from MiaAI-Lab

model  DeepSeek-v4.1-Flash-EXL3serving  vllm context  600,000 tok maxrecipe  MiaAI-Lab · EXL3MiaAI Lab  @MiaAI_lab

01Decode throughput

Per-request decode rate and aggregate throughput at each concurrency level. Output capped at 384 tokens, temperature 0, thinking disabled.

single-stream decode
29.26 tok/s
c1 · mean of 3 runs
best aggregate throughput
41.67 tok/s
c2 · all batches
decode budget
384 tok
per request, temp 0
concurrencyper-req decodemin – maxttft meanaggregate
c13 reqs · 3 batch(es)29.26 tok/s29.07 – 29.42 372.5 ms28.53 tok/s
c24 reqs · 2 batch(es)21.84 tok/s20.82 – 23.09 488.1 ms41.67 tok/s
c48 reqs · 2 batch(es)21.11 tok/s19.62 – 21.98 10,006.0 ms41.06 tok/s

02Prefill & TTFT by prompt size

Time to first token as the prompt grows. Cold = first request for that size; warm = repeated identical prompt (prefix-cached prefill).

prompt sizettft coldttft warmprefill rate
~256actual 186 tokens533.9 ms565.3 ms329 tok/s
~1,000actual 721 tokens1,187.4 ms418.4 ms1,723 tok/s
~4,000actual 2,798 tokens3,314.1 ms525.4 ms5,325 tok/s
~16,000actual 11,187 tokens12,153.4 ms12,021.9 ms931 tok/s
~64,000actual 44,710 tokens58,854.9 ms46,684.8 ms958 tok/s
~128,000actual 89,399 tokens110,602.3 ms64,033.9 ms1,396 tok/s

03Prefix cache effect

Identical ~16000-token prompt sent repeatedly. The ratio shows how much the serving stack saves on cache hits.

ttft cold
11,987.2 ms
first request, ~11,187 tokens
ttft repeated
12,077.8 ms
identical prompt, prefix cache hit
cache speedup
0.99×
cold / repeated TTFT