Radeon AI PRO R9700 32G — what LLMs can it run?
74 of 90 indexed models fit comfortably in 32GB at 4K context, each at the highest-quality quant that still leaves headroom.
The short answer for Radeon AI PRO R9700 32G
32 GB, 640 GB/s GDDR6. 74 of 90 models in this index fit comfortably at 4K context; 75 load at all.
- Biggest that fits
- Jamba 1.5 Mini — 27.2 GB at AWQ INT4 — 4.8 GB spare at 4K, so longer context comes out of a thin margin.
- Room to grow
- GLM-4.7-Flash — 19.2 GB at Q4_K_M — under 60% of the card, which leaves 12.8 GB for a long context window or a second process.
- Speed ceiling
- Llama 3.1 8B Instruct at Q4_K_M reads 4.6 GB of weights per token, so 640 GB/s puts a hard ceiling near 139 tok/s. That is arithmetic on two published numbers, not a benchmark — real throughput lands below it. No run on this card has been measured here, so there is nothing to compare the ceiling against.
- The wall you will hit
- Backend coverage, not capacity. At 640 GB/s the memory system is competitive with NVIDIA cards of the same size; what varies is whether your runtime has a working ROCm build for your kernel and card. llama.cpp and vLLM both ship official ROCm support — this is a setup question, not a hardware ceiling.
70B+ · 1
| Model | Quant | Est. VRAM | Headroom | tok/s on RTX 4090 |
|---|---|---|---|---|
| Jamba 1.5 Mini52B-A12B | AWQ INT4 | 27.24 GB | +4.8 GB | — |
32B · 23
| Model | Quant | Est. VRAM | Headroom | tok/s on RTX 4090 |
|---|---|---|---|---|
| Mixtral 8x7B Instruct47B MoE | AWQ INT4 | 24.95 GB | +7.1 GB | — |
| Seed-OSS 36B Instruct36B | Q5_K_M | 27.81 GB | +4.2 GB | — |
| Command R 35B35B | Q4_K_M | 22.86 GB | +9.1 GB | 42Estimated |
| Yi 1.5 34B Chat34B | Q4_K_M | 22.82 GB | +9.2 GB | 40Estimated |
| Qwen3 32B Instruct32B | Q4_K_M | 21.85 GB | +10.1 GB | 42 |
| Qwen2.5 32B Instruct32B | Q4_K_M | 21.69 GB | +10.3 GB | 44 |
| Qwen2.5-Coder 32B Instruct32B | Q4_K_M | 21.69 GB | +10.3 GB | 44Estimated |
| DeepSeek-R1-Distill-Qwen-32B32B | Q4_K_M | 21.69 GB | +10.3 GB | 42Estimated |
| Gemma 4 31B IT31B | Q4_K_M | 21.08 GB | +10.9 GB | — |
| Qwen3 30B-A3B Instruct30B-A3B | Q5_K_M | 23.04 GB | +9 GB | 82 |
| Qwen3-Coder 30B-A3B Instruct30B-A3B | Q5_K_M | 23.04 GB | +9 GB | 80 |
| Qwen3-VL 30B-A3B Instruct30B-A3B | Q4_K_M | 19.73 GB | +12.3 GB | 95Community |
| GLM-4.7-Flash30B-A3B | Q4_K_M | 19.19 GB | +12.8 GB | — |
| Qwen3.8 27B27B | Q4_K_M | 17.47 GB | +14.5 GB | — |
| Gemma 3 27B IT27B | Q4_K_M | 18.37 GB | +13.6 GB | 48Community |
| Gemma 2 27B Instruct27B | Q5_K_M | 21.76 GB | +10.2 GB | 42Estimated |
| Gemma 4 26B-A4B IT26B-A4B | Q4_K_M | 16.39 GB | +15.6 GB | — |
| Mistral Small 24B Instruct24B | Q4_K_M | 15.89 GB | +16.1 GB | 62Estimated |
| Devstral Small 1.1 24B24B | Q6_K | 20.91 GB | +11.1 GB | 48Estimated |
| Magistral Small 1.2 24B24B | Q6_K | 20.91 GB | +11.1 GB | 47Estimated |
| Codestral 22B22B | Q4_K_M | 15.03 GB | +17 GB | 58Estimated |
| ERNIE 4.5 21B-A3B21B-A3B | Q8_0 | 24.44 GB | +7.6 GB | — |
| GPT-OSS 20B21B MoE | MXFP4 | 12.76 GB | +19.2 GB | 195Community |
14B · 16
| Model | Quant | Est. VRAM | Headroom | tok/s on RTX 4090 |
|---|---|---|---|---|
| InternLM2 20B Chat20B | Q5_K_M | 15.59 GB | +16.4 GB | 68Estimated |
| DeepSeek-Coder-V2-Lite Instruct16B-A2.4B | Q8_0 | 17.56 GB | +14.4 GB | 118Estimated |
| DeepSeek-V2-Lite Chat16B-A2.4B | Q4_K_M | 10.08 GB | +21.9 GB | 142Estimated |
| StarCoder2 15B15B | Q4_K_M | 10.19 GB | +21.8 GB | 92Estimated |
| Qwen3 14B Instruct14B | Q5_K_M | 11.65 GB | +20.4 GB | 78 |
| Qwen2.5 14B Instruct14B | Q5_K_M | 11.73 GB | +20.3 GB | 86Estimated |
| DeepSeek-R1-Distill-Qwen-14B14B | Q4_K_M | 10.14 GB | +21.9 GB | 95 |
| Phi-4 14B14B | Q5_K_M | 11.77 GB | +20.2 GB | 78Estimated |
| Phi-3 Medium 14B Instruct14B | Q6_K | 12.86 GB | +19.1 GB | 88Estimated |
| Mistral Nemo 12B Instruct12B | Q6_K | 11.14 GB | +20.9 GB | 95Estimated |
| Gemma 3 12B IT12B | Q5_K_M | 9.84 GB | +22.2 GB | 92Estimated |
| Stable LM 2 12B Chat12B | Q4_K_M | 8.35 GB | +23.7 GB | 108Estimated |
| Llama 3.2 11B Vision Instruct11B | Q8_0 | 12.9 GB | +19.1 GB | 72Estimated |
| Solar 10.7B Instruct11B | Q4_K_M | 7.6 GB | +24.4 GB | 125Estimated |
| Falcon 3 10B Instruct10B | Q4_K_M | 7.28 GB | +24.7 GB | 118Estimated |
| Gemma 2 9B Instruct9B | Q8_0 | 11.7 GB | +20.3 GB | 108Estimated |
7B · 23
| Model | Quant | Est. VRAM | Headroom | tok/s on RTX 4090 |
|---|---|---|---|---|
| GLM-4-9B-Chat9B | Q8_0 | 10.16 GB | +21.8 GB | 105Estimated |
| Qwen3-VL 8B Instruct8B | Q8_0 | 10.39 GB | +21.6 GB | 108Estimated |
| Ministral 3 8B Instruct8B | Q8_0 | 10.02 GB | +22 GB | — |
| Qwen2-VL 7B Instruct7B | Q4_K_M | 5.49 GB | +26.5 GB | 72Estimated |
| Granite 3.1 8B Instruct8B | Q4_K_M | 5.88 GB | +26.1 GB | 142Estimated |
| Qwen3 8B Instruct8B | Q6_K | 7.64 GB | +24.4 GB | 122 |
| Llama 3.1 8B Instruct8B | Q8_0 | 9.47 GB | +22.5 GB | 118 |
| Nous Hermes 3 Llama 3.1 8B8B | Q4_K_M | 5.64 GB | +26.4 GB | 148Estimated |
| Aya 23 8B8B | Q4_K_M | 5.64 GB | +26.4 GB | 145Estimated |
| OpenChat 3.6 8B8B | Q4_K_M | 5.64 GB | +26.4 GB | 146Estimated |
| DeepSeek-R1-Distill-Llama-8B8B | Q5_K_M | 6.51 GB | +25.5 GB | 128Estimated |
| InternLM2 7B Chat7B | Q4_K_M | 5.45 GB | +26.6 GB | 148Estimated |
| Qwen2.5 7B Instruct7B | Q6_K | 6.77 GB | +25.2 GB | 132 |
| Qwen2.5-Coder 7B Instruct7B | Q4_K_M | 5.07 GB | +26.9 GB | 158Estimated |
| DeepSeek-R1-Distill-Qwen-7B7B | Q4_K_M | 5.07 GB | +26.9 GB | 152Estimated |
| Gemma 4 E4B ITE4B | Q8_0 | 8.46 GB | +23.5 GB | — |
| OLMo 2 7B Instruct7B | Q8_0 | 10.3 GB | +21.7 GB | 125Estimated |
| OLMo 3 7B Instruct7B | Q8_0 | 10.3 GB | +21.7 GB | — |
| WizardLM-2 7B7B | Q4_K_M | 5.14 GB | +26.9 GB | 152Estimated |
| Mistral 7B Instruct v0.37B | Q6_K | 6.75 GB | +25.3 GB | 135Estimated |
| Zephyr 7B Beta7B | Q6_K | 6.75 GB | +25.3 GB | 132Estimated |
| Gemma 4 E2B ITE2B | Q8_0 | 5.2 GB | +26.8 GB | — |
| Gemma 3 4B IT4B | Q8_0 | 5.05 GB | +27 GB | 145Estimated |
≤3B · 11
| Model | Quant | Est. VRAM | Headroom | tok/s on RTX 4090 |
|---|---|---|---|---|
| Qwen3 4B Instruct4B | Q6_K | 4.05 GB | +28 GB | 145 |
| Phi-4 Mini Instruct3.8B | Q8_0 | 4.81 GB | +27.2 GB | 262Estimated |
| Phi-3.5 Mini Instruct3.8B | Q8_0 | 5.89 GB | +26.1 GB | 255Estimated |
| Llama 3.2 3B Instruct3B | Q8_0 | 4.05 GB | +28 GB | 285Estimated |
| Qwen2.5 3B Instruct3B | Q8_0 | 3.59 GB | +28.4 GB | 290Estimated |
| SmolLM3 3B3B | Q8_0 | 3.73 GB | +28.3 GB | — |
| Gemma 2 2B Instruct2B | Q8_0 | 3.34 GB | +28.7 GB | 320Estimated |
| Qwen3 1.7B Instruct1.7B | Q8_0 | 2.39 GB | +29.6 GB | 240Estimated |
| Qwen2.5 1.5B Instruct1.5B | Q8_0 | 1.83 GB | +30.2 GB | 410Estimated |
| Llama 3.2 1B Instruct1B | Q8_0 | 1.51 GB | +30.5 GB | 450Estimated |
| Qwen2.5 0.5B Instruct0.5B | Q8_0 | 0.6 GB | +31.4 GB | 540Estimated |
How this list is built
Each row is the lowest-perplexity-loss quant of that model whose estimated total — weights plus KV cache at 4K context plus activation buffer — uses at most 88% of the card. That is the calculator's "green" threshold, so every row here has real headroom rather than only just fitting. Raise the context length and the list shortens; the calculator lets you check any combination directly.
A further 1 models load but with no headroom to spare (up to 105% of VRAM) — the Quant Hub’s GPU chips count those too, which is why its number is higher.
Measured on this card
No benchmark runs in this index were recorded on a Radeon AI PRO R9700 32G. Every figure on this page is calculated from the model architecture and the quant level — treat them as estimates, not measurements. The speed column in the tables above is an RTX 4090 figure shown for reference — it is not a speed on this card.
Cards with the same budget
What fits is decided by memory, so every 32GB card of this type returns the same list. These pages are not different answers — they differ in throughput, which this index does not measure per card.
The same 32GB in system memory is a different machine — everything on this page fits there, at a speed set by your DIMMs rather than a graphics bus: 32 GB RAM (CPU)
Setup guides for this card
Step-by-step guides written for the Radeon AI PRO R9700 32G's runtime or memory size, with the commands and the checks that prove the model is running on the card.
Stepping up
A Radeon PRO W7900 48G (48GB) fits 6 more of the indexed models than this card. Radeon PRO W7900 48G →
Common questions
What is the best local LLM for a Radeon AI PRO R9700 32G?
For everyday use, GLM-4.7-Flash at Q4_K_M — about 19.2 GB of the card's 32 GB at 4K context, leaving 12.8 GB for a longer window. If you want the largest thing that will load, that is Jamba 1.5 Mini at AWQ INT4 (27.2 GB, 4.8 GB spare). "Best" here means best fit for the memory budget — this index does not run task benchmarks, so it cannot tell you which model is smarter.
Can a Radeon AI PRO R9700 32G run Llama 3.1 70B Instruct?
No. Its smallest build here, AWQ INT4, needs about 38.3 GB and this card has 32.0 GB usable — short by 6.3 GB before any context beyond 4K. The largest model this card does clear is Jamba 1.5 Mini.
How many tokens per second does a Radeon AI PRO R9700 32G do on an 8B model at Q4?
This index has no measured run on this card, so it does not publish a figure. What can be stated from specifications: generating a token requires reading every weight once, Llama 3.1 8B Instruct at Q4_K_M is 4.6 GB of weights, and this card moves 640 GB/s — a ceiling near 139 tok/s. Batch size, context length, the runtime and how much of the model sits in cache all take you below it.
Radeon AI PRO R9700 32G or RTX 5090 for local LLMs?
They hold the same models: 32 GB against 32 GB fits 74 and 74 of 90 respectively at 4K. The difference is throughput — 640 GB/s against 1,792 GB/s, a 2.8× gap in how fast the weights can be read, which is what token generation is bound by. The RTX 5090 generates faster on any model both can hold.
74 of 90 indexed models fit comfortably in 32GB at 4K context, each at the highest-quality quant that still leaves headroom.
This page's figures change when the model or the runtime does.Last updated 2026-10-08 RSS → /feed.xml