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
| Quant | Runtime | Build | Decode tok/s (best run) | Workload | Runs | Source |
|---|---|---|---|---|---|---|
| Q4_0 | llama.cpp | 1 (9725a31) | 141.1median of 3 runs: 139.5 | chat-short | 3 | Measured by llm-speed@llmspeed |
| Q4_0 | llama.cpp | 8931 (9725a313b) | 130.6median of 3 runs: 130.2 | long-context-decay | 3 | Measured by llm-speed@llmspeed |
| Q4_0 | llama.cpp | 1 (9725a31) | 142.6median of 3 runs: 136.3 | long-context-decay | 3 | Community-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
| Run | Date (UTC) | Workload | Quant | Runtime / build | Prompt tokens | Output tokens | Decode tok/s | Prefill tok/s | TTFT ms | VRAM GiB | Source |
|---|---|---|---|---|---|---|---|---|---|---|---|
| r_v5arqmazf31 | 2026-09-05 | chat-short | Q4_0 | llama.cpp · 1 (9725a31) | 121 | 256 | 141.1 | 2027.9 | 60 | 7.75 | Measured by llm-speed@llmspeed |
| r_c2podhe9qwc | 2026-09-05 | chat-short | Q4_0 | llama.cpp · 1 (9725a31) | 121 | 256 | 139.5 | 1958.9 | 62 | 7.75 | Measured by llm-speed@llmspeed |
| r_8xmm65n1fhq | 2026-09-05 | chat-short | Q4_0 | llama.cpp · 1 (9725a31) | 121 | 256 | 137.3 | 2316.4 | 52 | 7.75 | Measured by llm-speed@llmspeed |
| r_c2podhe9qwc | 2026-09-05 | chat-long | Q4_0 | llama.cpp · 1 (9725a31) | 3188 | 620 | 137.1 | 5118.0 | 623 | 7.75 | Measured by llm-speed@llmspeed |
| r_v5arqmazf31 | 2026-09-05 | chat-long | Q4_0 | llama.cpp · 1 (9725a31) | 3188 | 620 | 135.6 | 5147.7 | 619 | 7.75 | Measured by llm-speed@llmspeed |
| r_8xmm65n1fhq | 2026-09-05 | chat-long | Q4_0 | llama.cpp · 1 (9725a31) | 3188 | 620 | 135.6 | 5523.3 | 577 | 7.75 | Measured by llm-speed@llmspeed |
| r_-lh-vczf7ik | 2026-09-06 | long-context-decay | Q4_0 | llama.cpp · 1 (9725a31) | 23872 | 98 | 142.6 | 7115.8 | 3355 | – | Community-submitted |
| r_1srqmpjnkgd | 2026-09-06 | long-context-decay | Q4_0 | llama.cpp · 1 (9725a31) | 23872 | 98 | 136.3 | 7161.0 | 3334 | – | Community-submitted |
| r_8q-uf-rq-gp | 2026-09-06 | long-context-decay | Q4_0 | llama.cpp · 1 (9725a31) | 23872 | 98 | 134.2 | 6022.4 | 3964 | – | Community-submitted |
| r_9suyze4n7lx | 2026-10-22 | long-context-decay | Q4_0 | llama.cpp · 8931 (9725a313b) | 23872 | 98 | 130.6 | 5881.5 | 4059 | – | Measured by llm-speed@llmspeed |
| r_fzn_lluvw14 | 2026-10-22 | long-context-decay | Q4_0 | llama.cpp · 8931 (9725a313b) | 23872 | 98 | 130.2 | 5891.1 | 4052 | – | Measured by llm-speed@llmspeed |
| r_kdmexe0lkqn | 2026-10-22 | long-context-decay | Q4_0 | llama.cpp · 8931 (9725a313b) | 23872 | 98 | 129.8 | 5965.8 | 4001 | – | 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
- Source runs: r_v5arqmazf31, r_c2podhe9qwc, r_8xmm65n1fhq, r_-lh-vczf7ik, r_1srqmpjnkgd, r_8q-uf-rq-gp, r_9suyze4n7lx, r_fzn_lluvw14, r_kdmexe0lkqn
- Gemma 4 12B IT QAT: every measured GPU and Mac
- RTX 5090: every measured model
- VRAM calculator for Gemma 4 12B IT QAT on RTX 5090
- RTX 5090 vs RTX 4090
- RTX 5090 vs RTX 3090
- How runs are measured · open dataset (CC BY 4.0)