Will It Run AI

Can Qwen3 8B DeepSeek v3.2 Speciale Distill run on Radeon Pro W6800 32GB?

YES — Runs Great

C47Usable
Estimated from fit model

Qwen3 8B DeepSeek v3.2 Speciale Distill needs ~9.9 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
Share:

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) — 9.9 GB, 58.8 tok/s, Runs well
9.9 GB required32.0 GB available
31% VRAM used

Fit status

Runs well

Decode

58.8 tok/s

TTFT

3295 ms

Safe context

393K

Memory

9.9 GB / 32.0 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen3 8B DeepSeek v3.2 Speciale Distill on Radeon Pro W6800 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: 58.8 tok/s decode · 3.3s TTFT (warm) · 147 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well58.8 tok/s1797 ms393K
CodingCRuns well58.8 tok/s3295 ms393K
Agentic CodingCRuns well58.8 tok/s4793 ms393K
ReasoningCRuns well58.8 tok/s3894 ms393K
RAGCRuns well58.8 tok/s5991 ms393K

Quantization options

How Qwen3 8B DeepSeek v3.2 Speciale Distill (8B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC43
Q3_K_S
3
3.9 GB
LowC43
NVFP4
4
4.5 GB
MediumC44
Q4_K_M
4
4.9 GB
MediumC44
Q5_K_M
5
5.8 GB
HighC44
Q6_K
6
6.6 GB
HighC44
Q8_0
8
8.6 GB
Very HighC45
F16Best for your GPU
16
16.4 GB
MaximumC49

Get started

Copy-paste commands to run Qwen3 8B DeepSeek v3.2 Speciale Distill on your machine.

Run

lms load hf-teichai--qwen3-8b-deepseek-v3-2-speciale-distill-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien Qwen3 8B DeepSeek v3.2 Speciale Distill

MacBook Pro M4 Max 48GBOpción económica
48 GB Unified (+16)546 GB/s (+34)
C
Sube la velocidad estimada de decodificación alrededor de un 31%.76.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 31%.

~$2,499 MSRP

MacBook Pro M3 Max 48GBMejor relación calidad-precio
48 GB Unified (+16)
C
Este setup está bastante equilibrado para este modelo.49.2 tok/s decodificación

~$2,499 MSRP

Frequently asked questions

Can Radeon Pro W6800 32GB run Qwen3 8B DeepSeek v3.2 Speciale Distill?

Yes, Radeon Pro W6800 32GB can run Qwen3 8B DeepSeek v3.2 Speciale Distill with a C grade (Runs well). Expected decode speed: 58.8 tok/s.

How much VRAM does Qwen3 8B DeepSeek v3.2 Speciale Distill need?

Qwen3 8B DeepSeek v3.2 Speciale Distill (8B parameters) requires approximately 9.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3 8B DeepSeek v3.2 Speciale Distill?

The recommended quantization for Qwen3 8B DeepSeek v3.2 Speciale Distill is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3 8B DeepSeek v3.2 Speciale Distill run at on Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, Qwen3 8B DeepSeek v3.2 Speciale Distill achieves approximately 58.8 tokens per second decode speed with a time-to-first-token of 3295ms using Q4_K_M quantization.

Can Radeon Pro W6800 32GB run Qwen3 8B DeepSeek v3.2 Speciale Distill for coding?

For coding workloads, Qwen3 8B DeepSeek v3.2 Speciale Distill on Radeon Pro W6800 32GB receives a C grade with 58.8 tok/s and 393K context.

What context window can Qwen3 8B DeepSeek v3.2 Speciale Distill use on Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, Qwen3 8B DeepSeek v3.2 Speciale Distill can safely use up to 393K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W6800 32GBSee all hardware for Qwen3 8B DeepSeek v3.2 Speciale Distill
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-teichai--qwen3-8b-deepseek-v3-2-speciale-distill-gguf-on-radeon-pro-w6800-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: