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Run LLM Locally

Qwen3 1.7B on the RX 7900 GRE

With 16 GB of VRAM, the RX 7900 GRE runs Qwen3 1.7B at Q4_K_M entirely on the GPU with an 8K context (about 3.2 GB needed). At Q8_0 it fits entirely.

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Qwen3 1.7B

Q4_K_M · 8K context

Excellent

The model needs about 3.2 GB and fits in the 15.5 GB available, with 12.3 GB to spare.

Your hardware
RX 7900 GRE 16 GB · 64 GB RAM
Estimated memory
~3.2 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
141–230 tok/s estimate
Largest context in fast memory
32K tokens
Confidence: low

Based on: memory bandwidth (576 GB/s) and model size

Memory breakdown

~3.2 GB (2.8 GB–3.6 GB) needed · estimate

Fits entirely in VRAM · 12.3 GB left

  • Model weights1.2 GB
  • KV cache0.9 GB
  • Runtime overhead0.8 GB
  • Safety margin0.3 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (ROCm) · flash attention on · weights = real file size

EstimateData updated 24 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • no benchmark for this hardware and model yet

Other quantizations on the RX 7900 GRE

Qwen3 1.7B on RX 7900 GRE + 64 GB RAM · 8K context
Qwen3 1.7B Q8_08K~3.7 GB · 118–187 tok/s estimateExcellent
Qwen3 1.7B Q6_K8K~3.6 GB · 124–198 tok/s estimateExcellent
Qwen3 1.7B Q5_K_M8K~3.4 GB · 132–213 tok/s estimateExcellent
Qwen3 1.7B Q4_K_M8K~3.2 GB · 141–230 tok/s estimateExcellent
Qwen3 1.7B IQ4_XS8K~3.1 GB · 146–241 tok/s estimateExcellent
Qwen3 1.7B Q3_K_M8K~3 GB · 152–253 tok/s estimateExcellent

Alternatives

GPUs that run Qwen3 1.7B Q4_K_M entirely in VRAM

Other models that fit the RX 7900 GRE

Where to buy a RX 7900 GRE

Prices and links follow the market you choose.

Radeon RX 7900 GRE

16 GB GDDR6 · 576 GB/s · 260 W

ExcellentRuns Qwen3 1.7B Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RX 7900 GRE run Qwen3 1.7B?
The model needs about 3.2 GB and fits in the 15.5 GB available, with 12.3 GB to spare.
Which quantization should I use on the RX 7900 GRE?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~3.7 GB / 15.5 GB.
What is the largest context on the RX 7900 GRE?
At Q4_K_M, about 32K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Qwen3 1.7B (RX 7900 GRE)?
We estimate 141–230 tok/s (estimate, confidence low). Based on: memory bandwidth (576 GB/s) and model size.