Will It Run AI

Can Qwen3 8B DeepSeek v3.2 Speciale Distill run on RX 9060 XT 16GB?

YES — Runs Great

C50Usable
Estimated from fit model

Qwen3 8B DeepSeek v3.2 Speciale Distill needs ~8.3 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~41 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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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) — 8.3 GB, 41.3 tok/s, Runs well
8.3 GB required16.0 GB available
52% VRAM used

Fit status

Runs well

Decode

41.3 tok/s

TTFT

4686 ms

Safe context

147K

Memory

8.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen3 8B DeepSeek v3.2 Speciale Distill on RX 9060 XT 16GB
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: 41.3 tok/s decode · 4.7s TTFT (warm) · 103 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 well41.3 tok/s2556 ms147K
CodingCRuns well41.3 tok/s4686 ms147K
Agentic CodingCRuns well41.3 tok/s6817 ms147K
ReasoningCRuns well41.3 tok/s5538 ms147K
RAGCRuns well41.3 tok/s8521 ms147K

Quantization options

How Qwen3 8B DeepSeek v3.2 Speciale Distill (8B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC47
Q3_K_S
3
3.9 GB
LowC48
NVFP4
4
4.5 GB
MediumC48
Q4_K_M
4
4.9 GB
MediumC49
Q5_K_M
5
5.8 GB
HighC50
Q6_K
6
6.6 GB
HighC50
Q8_0Best for your GPU
8
8.6 GB
Very HighC51
F16
16
16.4 GB
MaximumF0

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

Upgrade-Optionen

Hardware, die Qwen3 8B DeepSeek v3.2 Speciale Distill gut ausführt

RX 7900 XT 20GBGünstige Wahl
20 GB VRAM (+4)800 GB/s (+480)
C
Raises estimated decode speed by about 138%.98.4 tok/s Dekodierung

Raises estimated decode speed by about 138%.

Adds memory headroom for longer context windows and future model growth.

ca. $899 MSRP

NVIDIARTX A4500 20GBBestes Preis-Leistungs-Verhältnis
20 GB VRAM (+4)640 GB/s (+320)
C
Raises estimated decode speed by about 148%.102.3 tok/s Dekodierung

Raises estimated decode speed by about 148%.

Adds memory headroom for longer context windows and future model growth.

ca. $2,000 MSRP

Frequently asked questions

Can RX 9060 XT 16GB run Qwen3 8B DeepSeek v3.2 Speciale Distill?

Yes, RX 9060 XT 16GB can run Qwen3 8B DeepSeek v3.2 Speciale Distill with a C grade (Runs well). Expected decode speed: 41.3 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 8.3 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 9060 XT 16GB?

On RX 9060 XT 16GB, Qwen3 8B DeepSeek v3.2 Speciale Distill achieves approximately 41.3 tokens per second decode speed with a time-to-first-token of 4686ms using Q4_K_M quantization.

Can RX 9060 XT 16GB run Qwen3 8B DeepSeek v3.2 Speciale Distill for coding?

For coding workloads, Qwen3 8B DeepSeek v3.2 Speciale Distill on RX 9060 XT 16GB receives a C grade with 41.3 tok/s and 147K context.

What context window can Qwen3 8B DeepSeek v3.2 Speciale Distill use on RX 9060 XT 16GB?

On RX 9060 XT 16GB, Qwen3 8B DeepSeek v3.2 Speciale Distill can safely use up to 147K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 9060 XT 16GBSee all hardware for Qwen3 8B DeepSeek v3.2 Speciale Distill
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