QUEST-30B-RL-B70-Turbo
2recorded runs
94.6best tok/s
0verified
What the QUEST-30B-RL-B70-Turbo numbers mean
The fastest result on our board is 94.6 output tok/s on an Intel Arc Pro B70 at Q5_K_M with a 2048-token context. That is about 10.6 ms per generated token — 500 tokens takes roughly 5.3 seconds. Prompt processing on the same record runs at 1342.0 tok/s, so a 2,000-token prompt is read in about 1.5 seconds. Across the 2 records we hold for this model the range is 77.2–94.6 tok/s on 2 GPUs, because quantisation, context length and the engine build all move the number.
How much VRAM does QUEST-30B-RL-B70-Turbo need?
QUEST-30B-RL-B70-Turbo has 31.0B parameters. These estimates use the same formula as the VRAM calculator: weights, plus about 15% for a moderate-context KV cache and runtime overhead, plus a small engine floor.
| Quantisation | Estimated VRAM | Smallest card that fits |
|---|---|---|
| Q4_K_M (4-bit) | 20.9 GB | 32 GB |
| Q8_0 (8-bit) | 38.7 GB | 48 GB |
| FP16 (16-bit) | 72.1 GB | 96 GB |
Fastest GPUs for this model (best tok/s)
Models with a similar footprint
| Model | Params | Q4_K_M weights |
|---|---|---|
| Qwen3-30B-A3B-Instruct-2507 | 31B | 17.4 GB |
| Qwen3-30B-A3B-Thinking-2507 | 31B | 17.4 GB |
| Hy-MT2-30B-A3B | 30B | 16.9 GB |
| Muse-Glimmer-30B | 30B | 16.9 GB |
| Nemotron-3-Nano-Omni-30B-A3B-Reasoning | 30B | 16.9 GB |
| Nemotron-Cascade-2-30B-A3B | 32B | 18.0 GB |
| GPU | Model | Quant | Backend | Ctx | tok/s | prompt tok/s | VRAM GB | Source |
|---|---|---|---|---|---|---|---|---|
| Intel Arc Pro B70 · 32 GB | QUEST-30B-RL-B70-Turbo | Q5_K_M | llama.cpp — | 2048 | 94.6 … | 1342.0 | — | src |
| Intel Arc Pro B60 · 24 GB | QUEST-30B-RL-B70-Turbo | Q5_K_M | llama.cpp — | 2048 | 77.2 … | 998.6 | — | src |