Radeon Pro W7900 48GBコスパ重視
48 GB VRAM (+16)864 GB/s (+224)
C
Raises estimated decode speed by about 35%.23.9 tok/sデコード
Raises estimated decode speed by about 35%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
Can Qwen3.5 35B A3B run on Radeon AI PRO R9700 32GB?
YES — Tight Fit
C49Usable
Estimated from fit model
Qwen3.5 35B A3B needs ~29.6 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~18 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 29.6 GB, 17.7 tok/s, Tight fit
29.6 GB required32.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
17.7 tok/s
TTFT
10946 ms
Safe context
26K
Memory
29.6 GB / 32.0 GB
Memory breakdown
Weights21.3 GB
KV Cache4.1 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsQwen3.5 35B A3B on Radeon AI PRO R9700 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 17.7 tok/s decode · 10.9s TTFT (warm) · 44 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 17.7 tok/s | 5971 ms | 26K |
| Coding | C | Tight fit | 17.7 tok/s | 10946 ms | 26K |
| Agentic Coding | C | Runs with offload (needs ~1 GB host RAM) | 12.2 tok/s | 23074 ms | 26K |
| Reasoning | C | Tight fit | 17.7 tok/s | 12937 ms | 26K |
| RAG | C | Runs with offload (needs ~1 GB host RAM) | 12.2 tok/s | 28843 ms | 26K |
Quantization options
How Qwen3.5 35B A3B (35B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.7 GB | Low | C48 |
Q3_K_S | 3 | 17.2 GB | Low | C50 |
NVFP4 | 4 | 19.6 GB | Medium | C49 |
Q4_K_M | 4 | 21.3 GB | Medium | C49 |
Q5_K_MBest for your GPU | 5 | 25.2 GB | High | C49 |
Q6_K | 6 | 28.7 GB | High | F0 |
Q8_0 | 8 | 37.5 GB | Very High | F0 |
F16 | 16 | 71.8 GB | Maximum | F0 |
Get started
Copy-paste commands to run Qwen3.5 35B A3B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "lmstudio-community/Qwen3.5-35B-A3B-GGUF" \
--hf-file "Qwen3.5-35B-A3B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99アップグレードオプション
Qwen3.5 35B A3Bを快適に動かすハードウェア
Radeon PRO W7900 DS 48GBコスパ最良
48 GB VRAM (+16)864 GB/s (+224)
C
Raises estimated decode speed by about 35%.23.9 tok/sデコード
Raises estimated decode speed by about 35%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
AMD Instinct MI210 64GBAMDアップグレード
64 GB VRAM (+32)1638 GB/s (+998)
C
Raises estimated decode speed by about 195%.52.2 tok/sデコード
Raises estimated decode speed by about 195%.
Adds memory headroom for longer context windows and future model growth.
〜$10,000 MSRP
Frequently asked questions
Can Radeon AI PRO R9700 32GB run Qwen3.5 35B A3B?
Yes, Radeon AI PRO R9700 32GB can run Qwen3.5 35B A3B with a C grade (Tight fit). Expected decode speed: 17.7 tok/s.
How much VRAM does Qwen3.5 35B A3B need?
Qwen3.5 35B A3B (35B parameters) requires approximately 29.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen3.5 35B A3B?
The recommended quantization for Qwen3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen3.5 35B A3B run at on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, Qwen3.5 35B A3B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10946ms using Q4_K_M quantization.
Can Radeon AI PRO R9700 32GB run Qwen3.5 35B A3B for coding?
For coding workloads, Qwen3.5 35B A3B on Radeon AI PRO R9700 32GB receives a C grade with 17.7 tok/s and 26K context.
What context window can Qwen3.5 35B A3B use on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, Qwen3.5 35B A3B can safely use up to 26K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Qwen3.5 35B A3B feels slow on Radeon AI PRO R9700 32GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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