willitrun·ai

Can DeepSeek R1 Distill 32B run on Radeon PRO W7600 8GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

DeepSeek R1 Distill 32B needs ~25.1 GB but Radeon PRO W7600 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 25.1 GB, exceeds 8.0 GB available
25.1 GB required8.0 GB available
314% VRAM needed

17.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

4K

Memory

25.1 GB / 8.0 GB

Offload

70%

Memory breakdown

Weights19.5 GB
KV Cache3.9 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsDeepSeek R1 Distill 32B on Radeon PRO W7600 8GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 25.1 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy2.0 tok/s52800 ms4K
CodingFToo heavy2.0 tok/s96800 ms4K
Agentic CodingFToo heavy2.0 tok/s140800 ms4K
ReasoningFToo heavy2.0 tok/s114400 ms4K
RAGFToo heavy2.0 tok/s176000 ms4K

Inference speed

DeepSeek R1 Distill 32B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek R1 Distill 32B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~66 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M66.4Tight
MacBook Pro M4 Max 128GB
128 GBQ4_K_M33.2Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M33.2Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M30.8Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M25.7Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M24.8Heavy offload
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M24.3Fits
RX 7900 XTX 24GB
24 GBQ4_K_M22.9Heavy offload
NVIDIARTX 3090 24GB
24 GBQ4_K_M21.2Heavy offload
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M20.9Tight
MacBook Pro M3 Max 64GB
64 GBQ4_K_M13.3Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M12.2Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M9.0Too big
NVIDIARTX 4070 12GB
12 GBQ4_K_M3.1Too big
NVIDIARTX 3060 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 4060 8GB
8 GBQ4_K_M2.0Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How DeepSeek R1 Distill 32B (32B params) fits at each quantization level on Radeon PRO W7600 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowF0
Q3_K_S
3
15.7 GB
LowF0
NVFP4
4
17.9 GB
MediumF0
Q4_K_M
4
19.5 GB
MediumF0
Q5_K_M
5
23.0 GB
HighF0
Q6_K
6
26.2 GB
HighF0
Q8_0
8
34.2 GB
Very HighF0
F16
16
65.6 GB
MaximumF0

升级选项

能流畅运行 DeepSeek R1 Distill 32B 的硬件

RX 7900 XTX 24GB最佳性价比
24 GB VRAM (+16)960 GB/s (+672)
B
Makes the model fit on the accelerator instead of staying completely out of reach.22.9 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 1045%.

~$999 MSRP

Radeon AI PRO R9700 32GB经济之选
32 GB VRAM (+24)640 GB/s (+352)
A
Makes the model fit on the accelerator instead of staying completely out of reach.20.9 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,899 MSRP

Radeon Pro W6800 32GBAMD 升级
32 GB VRAM (+24)512 GB/s (+224)
A
Makes the model fit on the accelerator instead of staying completely out of reach.15.9 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,249 MSRP

NVIDIANVIDIA A100 40GB最大飞跃
40 GB VRAM (+32)1555 GB/s (+1267)
A
Makes the model fit on the accelerator instead of staying completely out of reach.72.3 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$10,000 MSRP

Frequently asked questions

Can Radeon PRO W7600 8GB run DeepSeek R1 Distill 32B?

No, DeepSeek R1 Distill 32B requires more memory than Radeon PRO W7600 8GB provides.

How much VRAM does DeepSeek R1 Distill 32B need?

DeepSeek R1 Distill 32B (32B parameters) requires approximately 25.1 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill 32B?

The recommended quantization for DeepSeek R1 Distill 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill 32B run at on Radeon PRO W7600 8GB?

On Radeon PRO W7600 8GB, DeepSeek R1 Distill 32B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can Radeon PRO W7600 8GB run DeepSeek R1 Distill 32B for coding?

For coding workloads, DeepSeek R1 Distill 32B on Radeon PRO W7600 8GB receives a F grade with 2.0 tok/s and 4K context.

What context window can DeepSeek R1 Distill 32B use on Radeon PRO W7600 8GB?

On Radeon PRO W7600 8GB, DeepSeek R1 Distill 32B can safely use up to 4K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek R1 Distill 32B feels slow on Radeon PRO W7600 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for Radeon PRO W7600 8GBSee all hardware for DeepSeek R1 Distill 32B
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