Can Qwen3 8B DeepSeek v3.2 Speciale Distill run on RX 7700 XT 12GB?

YES — Runs Great

C54Usable

Estimated from fit model

Qwen3 8B DeepSeek v3.2 Speciale Distill needs ~7.9 GB VRAM. RX 7700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.9 GB, 53.1 tok/s, Runs well

7.9 GB required12.0 GB available

66% VRAM used

Fit status

Runs well

Decode

53.1 tok/s

TTFT

3645 ms

Safe context

86K

Memory

7.9 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsQwen3 8B DeepSeek v3.2 Speciale Distill on RX 7700 XT 12GB
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: 53.1 tok/s decode · 3.6s TTFT (warm) · 133 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	53.1 tok/s	1988 ms	86K
Coding	C	Runs well	53.1 tok/s	3645 ms	86K
Agentic Coding	B	Runs well	53.1 tok/s	5302 ms	86K
Reasoning	C	Runs well	53.1 tok/s	4308 ms	86K
RAG	B	Runs well	53.1 tok/s	6627 ms	86K

Quantization options

How Qwen3 8B DeepSeek v3.2 Speciale Distill (8B params) fits at each quantization level on RX 7700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C49
Q3_K_S	3	3.9 GB	Low	C51
NVFP4	4	4.5 GB	Medium	C51
Q4_K_M	4	4.9 GB	Medium	C52
Q5_K_M	5	5.8 GB	High	C52
Q6_K	6	6.6 GB	High	C52
Q8_0Best for your GPU	8	8.6 GB	Very High	C52
F16	16	16.4 GB	Maximum	F0

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

Frequently asked questions

Can RX 7700 XT 12GB run Qwen3 8B DeepSeek v3.2 Speciale Distill?

Yes, RX 7700 XT 12GB can run Qwen3 8B DeepSeek v3.2 Speciale Distill with a C grade (Runs well). Expected decode speed: 53.1 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 7.9 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 RX 7700 XT 12GB?

On RX 7700 XT 12GB, Qwen3 8B DeepSeek v3.2 Speciale Distill achieves approximately 53.1 tokens per second decode speed with a time-to-first-token of 3645ms using Q4_K_M quantization.

Can RX 7700 XT 12GB run Qwen3 8B DeepSeek v3.2 Speciale Distill for coding?

For coding workloads, Qwen3 8B DeepSeek v3.2 Speciale Distill on RX 7700 XT 12GB receives a C grade with 53.1 tok/s and 86K context.

What context window can Qwen3 8B DeepSeek v3.2 Speciale Distill use on RX 7700 XT 12GB?

On RX 7700 XT 12GB, Qwen3 8B DeepSeek v3.2 Speciale Distill can safely use up to 86K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7700 XT 12GB See all hardware for Qwen3 8B DeepSeek v3.2 Speciale Distill

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<iframe src="https://willitrunai.com/embed/hf-teichai--qwen3-8b-deepseek-v3-2-speciale-distill-gguf-on-rx-7700-xt-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

53.1 tok/s

1988 ms

86K

Coding

Runs well

53.1 tok/s

3645 ms

86K

Agentic Coding

Runs well

53.1 tok/s

5302 ms

86K

Reasoning

Runs well

53.1 tok/s

4308 ms

86K

RAG

Runs well

53.1 tok/s

6627 ms

86K

Quantization options

How Qwen3 8B DeepSeek v3.2 Speciale Distill (8B params) fits at each quantization level on RX 7700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C49
Q3_K_S	3	3.9 GB	Low	C51
NVFP4	4	4.5 GB	Medium	C51
Q4_K_M	4	4.9 GB	Medium	C52
Q5_K_M	5	5.8 GB	High	C52
Q6_K	6	6.6 GB	High	C52
Q8_0Best for your GPU	8	8.6 GB	Very High	C52
F16	16	16.4 GB	Maximum	F0