Qwen2.5 Coder 1.5B Instruct

Qwen2.5 Coder 1.5B, a small code model that fits any 8 GB machine. Chat in your browser, plus an OpenAI-compatible API.

Engine: llama.cpp · Q4_K_M
Size: 1.1 GB
GPU memory: 3.5 GiB or more
Runs on

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Max

Picked Apple M4 Pro (20-core GPU): the lowest price that fits. No speed data for these GPUs yet.

3 machines can run this now · all new hosts1× Apple M4 Pro (20-core GPU)$0.12/hRecommended48 GB unified · 36 GB for AI 🇨🇭 Switzerland Dedicated 1× Apple M3 Max (30-core GPU)$0.13/h36 GB unified · 28 GB for AI 🇮🇳 India Dedicated 1× Apple M5 Max (40-core GPU)$0.26/h48 GB unified · 37 GB for AI 🇺🇸 United States Dedicated

On 1× Apple M4 Pro (20-core GPU). Runs 1 h, then stops · holds $0.115 · incl. 10% fee

Deploying accepts the rental terms. Unused budget comes back to your balance.

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Verified hosts only

Options

Duration limit1 h

Duration in hoursHard cap: the machine stops at this limit even if you forget. Snaps to 30-minute steps up to a day, whole days after.

Budget cap

Held when it starts, never exceeded. Unused budget comes back.

Add SSH public keyOptional. You chat in the browser without one. Add it to also connect with SSH.

Hardware that can run this

M4 Pro · 24 GB unifiedQ4_K_M, Q8, FP16 · est. 146–208 tok/sDeployable now · $0.12/h

19 more devices could run it, none online

Hardware	Fits	Est. speed
RTX 3080 Ti · 12 GB	Q4_K_M, Q8, FP16	489–694 tok/s
M1 · 16 GB unified	Q4_K_M, Q8, FP16	Evidence unavailable: no cited memory bandwidth for this hardware
M1 Pro · 16 GB unified	Q4_K_M, Q8, FP16	107–153 tok/s
M2 · 16 GB unified	Q4_K_M, Q8, FP16	53–77 tok/s
M2 Pro · 16 GB unified	Q4_K_M, Q8, FP16	107–153 tok/s
M3 · 16 GB unified	Q4_K_M, Q8, FP16	53–77 tok/s
M4 · 16 GB unified	Q4_K_M, Q8, FP16	64–92 tok/s
RX 6800 XT · 16 GB	Q4_K_M, Q8, FP16	Evidence unavailable: no cited memory bandwidth for this hardware
M3 Pro · 18 GB unified	Q4_K_M, Q8, FP16	80–115 tok/s
RTX 3090 · 24 GB	Q4_K_M, Q8, FP16	502–713 tok/s
RTX 4090 · 24 GB	Q4_K_M, Q8, FP16	541–767 tok/s
RX 7900 XTX · 24 GB	Q4_K_M, Q8, FP16	Evidence unavailable: no cited memory bandwidth for this hardware
M1 Max · 32 GB unified	Q4_K_M, Q8, FP16	214–305 tok/s
M2 Max · 32 GB unified	Q4_K_M, Q8, FP16	214–305 tok/s
M3 Max · 48 GB unified	Q4_K_M, Q8, FP16	161–229 tok/s
M4 Max · 48 GB unified	Q4_K_M, Q8, FP16	220–312 tok/s
M4 Pro · 48 GB unified	Q4_K_M, Q8, FP16	146–208 tok/s
Instinct MI210 · 64 GB	Q4_K_M, Q8, FP16	Evidence unavailable: no cited memory bandwidth for this hardware
M4 Max · 64 GB unified	Q4_K_M, Q8, FP16	220–312 tok/s

Speeds are estimates from memory bandwidth and weight size, not measured benchmarks. A price is the cheapest listed offer for that hardware.

Template details

Rating: No ratings yet
Runs: 0
Min VRAM: 3.8 GB
Backend: Metal on Apple silicon
Weights: 1.1 GB
Version: llamacpp-qwen2-5-coder-1-5b-instruct-v1.0.0
Image digest: 1f4b9cf58982…
Health check: SSH probe on the leased endpoint

We match GPUs on their measured runtime and dedicated VRAM. A hardware report doesn't qualify another vendor's backend.

#llm #chat #openai-api #gguf #coding

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