Hardware profile
Current generationAMD Radeon AI PRO R9700 · 32 GB
32 GB VRAM · 640 GB/s · verified
256-bit GDDR6 20Gbps ≈640GB/s (gpu-monkey lists 645) Current-generation discrete GPU profile. VRAM is the hard capacity limit; system RAM is separate and relevant only when the runtime supports offload. The approximately 640 GB/s bandwidth figure is an audit estimate and is marked unverified; confirm it against the final vendor specification.
Audit estimate: bandwidthGbps is retained from the hardware audit but not yet confirmed on the linked vendor specification.
Recommended quantization by model
This table chooses the highest-quality published quantization that still clears the selected 8K context estimate. It does not compare output quality or claim that every backend has the same kernels.
| Model | Recommended quantization | Estimated requirement | Context ceiling | Result |
|---|---|---|---|---|
| Flint 40B | UD-Q3_K_XL | 20.2 GB at 8K | 262,144 tokens | Comfortable fit |
| NVIDIA Nemotron 3.5 Bolt E4B | Q4_K_M | 8.0 GB at 8K | 65,536 tokens | Comfortable fit |
| LFM3-1.7B | Q8_0 | 3.4 GB at 8K | 262,144 tokens | Comfortable fit |
| Bonsai 2 | PQ2_0 | 9.2 GB at 8K | 262,144 tokens | Comfortable fit |
| Qwen3.8 27B | Q4_K_M | 19.4 GB at 8K | 200,001 tokens | Comfortable fit |
| Gemma 4 12B Unified | Q8_0 | 14.7 GB at 8K | 262,144 tokens | Comfortable fit |
| Gemma 4 E4B | Q8_0 | 11.0 GB at 8K | 131,072 tokens | Comfortable fit |
| gpt-oss-20b | MXFP4 | 13.8 GB at 8K | 131,072 tokens | Comfortable fit |
| Qwen3.6 35B-A3B | Q4_K_M | 24.7 GB at 8K | 262,144 tokens | Comfortable fit |
| Gemma 4 31B | Q4_K_M | 22.2 GB at 8K | 76,562 tokens | Comfortable fit |
| Qwen3 8B | Q8_0 | 11.6 GB at 8K | 32,768 tokens | Comfortable fit |
| DeepSeek R1 8B | Q8_0 | 11.1 GB at 8K | 131,072 tokens | Comfortable fit |
| Llama 3.2 3B | Q8_0 | 5.8 GB at 8K | 131,072 tokens | Comfortable fit |
| Phi-4 Mini | Q8_0 | 6.7 GB at 8K | 131,072 tokens | Comfortable fit |
| Mistral 7B | Q8_0 | 10.3 GB at 8K | 32,768 tokens | Comfortable fit |
| Gemma 3 4B | Q8_0 | 7.6 GB at 8K | 131,072 tokens | Comfortable fit |
| gpt-oss-120b | MXFP4 | 65.2 GB at 8K | 0 tokens | Won't fit |
Per-quantization fit for recent models
These are the five most relevant non-server models plus the next current model in the catalog. Each row uses the same 8K formula, so changing a quantization or context length produces an arithmetic result rather than a new benchmark claim.
| Model | Quantization | Weight files | 8K requirement | Result | 8K context ceiling | Speed band | Ollama |
|---|---|---|---|---|---|---|---|
| Flint 40B | UD-IQ1_M | 11.35 GB | 13.1 GB | Comfortable fit | 262,144 tokens | Estimate · Very fast relative band | No verified tag |
| Flint 40B | UD-IQ2_XXS | 11.86 GB | 13.6 GB | Comfortable fit | 262,144 tokens | Estimate · Very fast relative band | No verified tag |
| Flint 40B | UD-Q2_K_XL | 13.56 GB | 15.3 GB | Comfortable fit | 262,144 tokens | Estimate · Very fast relative band | No verified tag |
| Flint 40B | UD-IQ3_XXS | 14.54 GB | 16.3 GB | Comfortable fit | 262,144 tokens | Estimate · Very fast relative band | No verified tag |
| Flint 40B | UD-Q3_K_XL | 18.49 GB | 20.2 GB | Comfortable fit | 262,144 tokens | Estimate · Very fast relative band | No verified tag |
| Flint 40B | NVFP4 | 22.86 GB | 24.6 GB | Comfortable fit | 262,144 tokens | Estimate · Very fast relative band | ollama run flint:40b |
| Flint 40B | UD-Q4_K_XL | 25.05 GB | 26.8 GB | Tight fit | 189,902 tokens | Estimate · Very fast relative band | No verified tag |
| Flint 40B | UD-Q5_K_XL | 29.92 GB | 31.7 GB | Tight fit | 20,244 tokens | Estimate · Very fast relative band | No verified tag |
| Flint 40B | UD-Q6_K_XL | 36.32 GB | 38.1 GB | Won't fit | 0 tokens | Estimate · Very fast relative band | No verified tag |
| Flint 40B | Q8_0 | 42.30 GB | 44.0 GB | Won't fit | 0 tokens | Estimate · Very fast relative band | No verified tag |
| NVIDIA Nemotron 3.5 Bolt E4B | Q4_K_M | 6.18 GB | 8.0 GB | Comfortable fit | 65,536 tokens | Estimate · Very fast relative band | ollama run nemotron-3.5-bolt:e4b |
| LFM3-1.7B | QAD-Q4_0 | 1.05 GB | 2.7 GB | Comfortable fit | 262,144 tokens | Estimate · Very fast relative band | ollama run lfm3:1.7b-qad-q4_0 |
| LFM3-1.7B | Q4_K_M | 1.10 GB | 2.7 GB | Comfortable fit | 262,144 tokens | Estimate · Very fast relative band | ollama run lfm3:1.7b-q4_K_M |
| LFM3-1.7B | Q5_K_M | 1.26 GB | 2.9 GB | Comfortable fit | 262,144 tokens | Estimate · Very fast relative band | No verified tag |
| LFM3-1.7B | Q6_K | 1.43 GB | 3.0 GB | Comfortable fit | 262,144 tokens | Estimate · Very fast relative band | No verified tag |
| LFM3-1.7B | Q8_0 | 1.85 GB | 3.4 GB | Comfortable fit | 262,144 tokens | Estimate · Very fast relative band | ollama run lfm3:1.7b-q8_0 |
| Bonsai 2 | PTQ1_0 | 5.95 GB | 8.0 GB | Comfortable fit | 262,144 tokens | Estimate · Moderate relative band | No verified tag |
| Bonsai 2 | PQ2_0 | 7.21 GB | 9.2 GB | Comfortable fit | 262,144 tokens | Estimate · Moderate relative band | No verified tag |
| Bonsai 2 | Q2_0 | 7.63 GB | 9.7 GB | Comfortable fit | 262,144 tokens | Estimate · Moderate relative band | ollama run bonsai-2:27b-q2_0 |
| Qwen3.8 27B | Q4_K_M | 17.39 GB | 19.4 GB | Comfortable fit | 200,001 tokens | Estimate · Moderate relative band | ollama run qwen3.8:27b-q4_K_M |
| Qwen3.8 27B | Q8_0 | 29.98 GB | 32.0 GB | Won't fit | 8,010 tokens | Estimate · Moderate relative band | ollama run qwen3.8:27b-q8_0 |
| Gemma 4 12B Unified | Q4_0 | 7.15 GB | 9.1 GB | Comfortable fit | 262,144 tokens | Estimate · Fast relative band | ollama run gemma4:12b-it-qat |
| Gemma 4 12B Unified | Q4_K_M | 7.30 GB | 9.2 GB | Comfortable fit | 262,144 tokens | Estimate · Fast relative band | ollama run gemma4:12b-it-q4_K_M |
| Gemma 4 12B Unified | Q8_0 | 12.85 GB | 14.7 GB | Comfortable fit | 262,144 tokens | Estimate · Fast relative band | ollama run gemma4:12b-it-q8_0 |
Speed bands are relative heuristics, not measured tokens per second. Verify the package name, license, backend support, and free memory before relying on a result.
Why this memory result is not a guarantee
AMD Radeon AI PRO R9700 · 32 GB has 32 GB of VRAM. The formula subtracts weight files, KV cache, and 1.5 GB of runtime overhead, then requires 20% remaining headroom for a comfortable result. Driver reservations, display buffers, other applications, longer prompts, concurrent requests, and backend-specific workspace can all reduce the usable budget.
Low-cost setup upgrades
Useful accessories around AMD Radeon AI PRO R9700 · 32 GB
These optional setup upgrades address storage, cooling, or power resilience. None increases GPU or unified-memory capacity.
External NVMe SSD for model storage
Keeps large GGUF and MLX files off the boot drive. Fast storage reduces load time, but it does not increase the memory available for inference.
Search current listings on AmazonDesktop RAM upgrade kit
Useful for MoE CPU offload and keeping the operating system out of VRAM. Match the motherboard's memory type, slot layout, capacity limit, and validated part numbers.
Search current listings on AmazonGPU support bracket
A lower-cost mechanical upgrade for heavy three-slot cards. Check clearance, mounting height, and whether the bracket blocks fans or case panels.
Search current listings on AmazonAs an Amazon Associate I earn from qualifying purchases. Links are sponsored search links and do not change the price you pay. Affiliate disclosure.
Specific model checks
No curated can-i-run page is published for this hardware profile yet.
Compare current hardware
The closest current-generation profiles by memory capacity are below. A higher tier can increase the context ceiling or make a larger quantization comfortable, but it does not automatically guarantee faster generation.
Sources and verification
Data reviewed . Check the linked vendor material for the exact configuration before buying or installing.
FAQ
Questions about AMD Radeon AI PRO R9700 · 32 GB.
How much VRAM does AMD Radeon AI PRO R9700 · 32 GB provide for local AI?
AMD Radeon AI PRO R9700 · 32 GB is listed with 32 GB of VRAM and 640 GB/s of memory bandwidth. The operating system, display buffers, and other applications share that capacity, so the model budget is always lower than the bare hardware number.
Which current local models fit comfortably on AMD Radeon AI PRO R9700 · 32 GB?
Flint 40B, NVIDIA Nemotron 3.5 Bolt E4B, LFM3-1.7B. Comfortable means the 8K estimate leaves at least 20% headroom after weights, KV cache, and runtime overhead. A larger or higher-quality quantization may move an individual result into the tight band.
What context length can Flint 40B at UD-Q3_K_XL use on this machine?
At the selected quantization, the formula reaches 262,144 tokens before the machine's 32 GB capacity is exhausted. The model's native context limit still caps the result, and real workloads may run out earlier because of fragmentation, other applications, or backend workspace.
Is the 640 GB/s bandwidth figure a measured token speed?
No. Very fast relative band is a relative estimate based on active parameters and memory bandwidth. It is not a benchmark: backend kernels, batch size, thermals, CPU offload, and concurrent work can change real generation speed substantially.
What should I do if a model does not fit on AMD Radeon AI PRO R9700 · 32 GB?
Try a lower-bit published quantization and a shorter context first. If the minimum requirement still exceeds 32 GB, the arithmetic gap to a 20% comfort target is about 46.2 GB. The next same-family current profile in this catalog is NVIDIA RTX PRO 6000 Blackwell · 96 GB.