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llm-speed

Can an RTX 5090 run Gemma 4 12B IT QAT?

Yes: an RTX 5090 runs Gemma 4 12B IT QAT at 141.1 tok/s at Q4_0, measured on llama.cpp with 16K context in September 2026.

9 signed runs on llm-speed. Single-request decode speed in tokens per second; the short-chat workload unless noted. 6 of 9 runs were measured by llm-speed (@llmspeed); the other 3 are community-submitted (signed on the submitter's own machine, not measured by llm-speed, and not used for llm-speed verdicts). Runs measured by llm-speed are credited to @llmspeed. Verdicts and headline numbers use only llm-speed's own runs and fit tests.

Measured tok/s by quant and runtime

QuantRuntimeBuildDecode tok/s (best run)WorkloadRunsSource
Q4_0llama.cpp1 (9725a31)141.1median of 3 runs: 139.5chat-short3Measured by llm-speed@llmspeed
Q4_0llama.cpp8931 (9725a313b)130.6median of 3 runs: 130.2long-context-decay3Measured by llm-speed@llmspeed
Q4_0llama.cpp1 (9725a31)142.6median of 3 runs: 136.3long-context-decay3Community-submitted

Fastest quant that fits: Q4_0 on llama.cpp, 141.1 tok/s (measured by llm-speed; it fits because it ran on this hardware). Quants without a run are not listed.

VRAM used: 7.75 GiB runtime-reported GPU allocation for model, context and compute buffers with a 16,384-token context configured (r_v5arqmazf31; a runtime-log allocation, not peak device memory).

Every measured row

RunDate (UTC)WorkloadQuantRuntime / buildPrompt tokensOutput tokensDecode tok/sPrefill tok/sTTFT msVRAM GiBSource
r_v5arqmazf312026-09-05chat-shortQ4_0llama.cpp · 1 (9725a31)121256141.12027.9607.75Measured by llm-speed@llmspeed
r_c2podhe9qwc2026-09-05chat-shortQ4_0llama.cpp · 1 (9725a31)121256139.51958.9627.75Measured by llm-speed@llmspeed
r_8xmm65n1fhq2026-09-05chat-shortQ4_0llama.cpp · 1 (9725a31)121256137.32316.4527.75Measured by llm-speed@llmspeed
r_c2podhe9qwc2026-09-05chat-longQ4_0llama.cpp · 1 (9725a31)3188620137.15118.06237.75Measured by llm-speed@llmspeed
r_v5arqmazf312026-09-05chat-longQ4_0llama.cpp · 1 (9725a31)3188620135.65147.76197.75Measured by llm-speed@llmspeed
r_8xmm65n1fhq2026-09-05chat-longQ4_0llama.cpp · 1 (9725a31)3188620135.65523.35777.75Measured by llm-speed@llmspeed
r_-lh-vczf7ik2026-09-06long-context-decayQ4_0llama.cpp · 1 (9725a31)2387298142.67115.83355–Community-submitted
r_1srqmpjnkgd2026-09-06long-context-decayQ4_0llama.cpp · 1 (9725a31)2387298136.37161.03334–Community-submitted
r_8q-uf-rq-gp2026-09-06long-context-decayQ4_0llama.cpp · 1 (9725a31)2387298134.26022.43964–Community-submitted
r_9suyze4n7lx2026-10-22long-context-decayQ4_0llama.cpp · 8931 (9725a313b)2387298130.65881.54059–Measured by llm-speed@llmspeed
r_fzn_lluvw142026-10-22long-context-decayQ4_0llama.cpp · 8931 (9725a313b)2387298130.25891.14052–Measured by llm-speed@llmspeed
r_kdmexe0lkqn2026-10-22long-context-decayQ4_0llama.cpp · 8931 (9725a313b)2387298129.85965.84001–Measured by llm-speed@llmspeed

Questions

Can an RTX 5090 run Gemma 4 12B IT QAT?

Yes: an RTX 5090 runs Gemma 4 12B IT QAT at 141.1 tok/s at Q4_0, measured on llama.cpp with 16K context in September 2026.

How many tokens per second does Gemma 4 12B IT QAT get on an RTX 5090?

Decode speed measured by llm-speed, by quantization and runtime: Q4_0 on llama.cpp: 141.1 tok/s; Q4_0 on llama.cpp: 130.6 tok/s (long-context-decay). Based on 6 runs measured by llm-speed; single-request chat-short unless noted. 3 community-submitted runs are listed on the page, labelled, and not counted here.

What is the fastest quant of Gemma 4 12B IT QAT that fits on an RTX 5090?

Q4_0 on llama.cpp was the fastest measured setting at 141.1 tok/s; it fit because it ran. Other quants are listed only where a run measured them.

How much VRAM does Gemma 4 12B IT QAT use on an RTX 5090?

The runtime reported 7.75 GiB of GPU allocation for model, context and compute buffers with a 16,384-token context configured. That is a runtime-log allocation, not peak device memory.

Sources and related pages