Can I run Qwen3 30B-A3B (2507) on the GeForce RTX 5090?
Runs greatQ4_K_M fits in VRAM with 11.5 GB to spare: Runs great, est. 235.0 tok/s (188.0–282.0, calibrated estimate ±20%).
32 GB VRAM · 1,792.0 GB/s · FP16 104.8 TFLOPS. Assumes 32 GB of DDR5-5600 system RAM, Windows with this GPU driving the display, 8k context and an f16 KV cache.
Every quant of Qwen3 30B-A3B (2507) on the GeForce RTX 5090
| Quant | File size | Verdict | Speed | Memory | Runs as | Notes |
|---|---|---|---|---|---|---|
| Q2_K | 11.26 GB | Runs great | est. 235.0 tok/s188.0–282.0calibrated estimate ±20% | 13.2 / 32.0 GB | Full GPU | — |
| IQ4_XS | 16.38 GB | Runs great | est. 235.0 tok/s188.0–282.0calibrated estimate ±20% | 18.4 / 32.0 GB | Full GPU | — |
| Q4_K_Mbaseline | 18.56 GB | Runs great | est. 235.0 tok/s188.0–282.0calibrated estimate ±20% | 20.5 / 32.0 GB | Full GPU | — |
| Q8_0 | 32.48 GB | Won't run | — | Needs about 33.9 GB; 31.4 GB of VRAM and 28.0 GB of RAM are free | — | — |
Where the memory goes at Q4_K_M
- Weights
- 18.6 GB
- KV cache
- 0.8 GB
- Compute buffer
- 0.6 GB
- OS reserve
- 0.6 GB
- Free
- 11.5 GB
Context length vs. KV cache
| Context | KV f16 | KV q8_0 | KV q4_0 |
|---|---|---|---|
| 4k | 19.5 GBRuns great est. 235.0 tok/s188.0–282.0calibrated estimate ±20% | 19.3 GBRuns great est. 235.0 tok/s188.0–282.0calibrated estimate ±20% | 19.2 GBRuns great est. 235.0 tok/s188.0–282.0calibrated estimate ±20% |
| 8k | 19.9 GBRuns great est. 235.0 tok/s188.0–282.0calibrated estimate ±20% | 19.6 GBRuns great est. 235.0 tok/s188.0–282.0calibrated estimate ±20% | 19.4 GBRuns great est. 235.0 tok/s188.0–282.0calibrated estimate ±20% |
| 16k | 20.8 GBRuns great est. 222.8 tok/s178.3–267.4calibrated estimate ±20% | 20.1 GBRuns great est. 235.0 tok/s188.0–282.0calibrated estimate ±20% | 19.7 GBRuns great est. 235.0 tok/s188.0–282.0calibrated estimate ±20% |
| 32k | 22.6 GBRuns great est. 154.2 tok/s123.4–185.0calibrated estimate ±20% | 21.1 GBRuns great est. 216.1 tok/s172.9–259.3calibrated estimate ±20% | 20.3 GBRuns great est. 235.0 tok/s188.0–282.0calibrated estimate ±20% |
| 64k | 26.1 GBRuns great est. 95.4 tok/s76.3–114.5calibrated estimate ±20% | 23.1 GBRuns great est. 147.8 tok/s118.3–177.4calibrated estimate ±20% | 21.5 GBRuns great est. 209.8 tok/s167.8–251.7calibrated estimate ±20% |
| 128k | 33.1 GBRuns slowly est. 19.2 tok/s13.4–24.9theoretical estimate ±30% | 27.2 GBRuns great est. 90.6 tok/s72.5–108.7calibrated estimate ±20% | 23.9 GBRuns great est. 142.0 tok/s113.6–170.4calibrated estimate ±20% |
| 256k | 47.2 GBRuns slowly est. 19.2 tok/s13.4–24.9theoretical estimate ±30% | 35.2 GBRuns slowly est. 19.2 tok/s13.4–24.9theoretical estimate ±30% | 28.8 GBRuns great est. 86.2 tok/s69.0–103.5calibrated estimate ±20% |
S = Runs great · A = Runs well · B = Runs slowly · F = Won't run
At Q4_K_M the verdict stays Runs great up to 64k context with an f16 KV cache, and up to 128k with q8_0.
Estimated speed
- Generation
- est. 235.0 tok/s (188.0–282.0, calibrated estimate ±20%)
- Prompt processing
- about 14,672 tok/s (8,803–20,541, rough estimate ±40%)
Measured on this exact combination
Public benchmarks we calibrate against. Their conditions (context, backend, flags) can differ from the estimates above.
| Hardware | Quant | Backend | Context | Prompt (tok/s) | Generation (tok/s) | Flags | Source | Measured |
|---|---|---|---|---|---|---|---|---|
| GeForce RTX 5090 | Q4_K_XL | llama.cpp | 16k | — | 141.6 | — | hardware-corner.net | 2026-08-09 |
If this is not enough
A larger model that also runs well on the GeForce RTX 5090
Runs greatQwen3.5 35B-A3B: Runs great, est. 235.0 tok/s (188.0–282.0, calibrated estimate ±20%)
How to run it
Commands for the Q4_K_M file. Both tools download from Hugging Face on first run.
File: Qwen3-30B-A3B-Instruct-2507-Q4_K_M.gguf (18.56 GB) from unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF.
llama-server -hf unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF:Q4_K_M -c 8192 -ngl all -fa on- -ngl all loads every layer on the GPU.
- -fa on enables flash attention (needed for KV cache quantization).
- --cache-type-k q8_0 --cache-type-v q8_0 shrinks the KV cache to 0.43 GB at 8k context.
PowerShell (quit the Ollama tray app first):
$env:OLLAMA_CONTEXT_LENGTH="8192"; ollama serve
ollama run hf.co/unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF:Q4_K_M- Ollama defaults to 4k context on GPUs under 24 GB. Set OLLAMA_CONTEXT_LENGTH when starting the server, or /set parameter num_ctx inside the chat.
/set parameter num_ctx 8192
Related pages
Guide: How much VRAM do local LLMs need? 8–32 GB tiers (2026)
Other models on the GeForce RTX 5090
Qwen3 30B-A3B (2507) on other hardware
Frequently asked questions
Can I run Qwen3 30B-A3B (2507) on the GeForce RTX 5090?
Q4_K_M fits in VRAM with 11.5 GB to spare: Runs great, est. 235.0 tok/s (188.0–282.0, calibrated estimate ±20%). Q4_K_M needs 19.9 GB at 8k context; this setup has 31.4 GB of usable memory and 28.0 GB of free system RAM.
How much context can Qwen3 30B-A3B (2507) use on the GeForce RTX 5090?
At Q4_K_M the verdict stays Runs great up to 64k context with an f16 KV cache (6.4 GB of KV) and up to 128k with a q8_0 KV cache (6.9 GB). The model supports up to 256k.
Which quant should I use, and how fast is it?
Q4_K_M (18.56 GB) is the recommended balance: Runs great, est. 235.0 tok/s (188.0–282.0, calibrated estimate ±20%). It is also the largest tracked file that gets this verdict.
Speeds are estimates from memory bandwidth, calibrated against public benchmarks, and each one comes with an error band and a confidence label. Real results vary with drivers, backend, context length and thermals.