MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+16)546 GB/s (+34)
C
This setup is broadly balanced for this model.34.7 tok/s decode
~$2,499 MSRP
starcoder2 15b instruct v0.1 needs ~15.0 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~31 tok/s.
Operating mode
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.
Select quantization to explore
Fit status
Runs well
Decode
31.3 tok/s
TTFT
6178 ms
Safe context
171K
Memory
15.0 GB / 32.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 31.3 tok/s | 3370 ms | 171K |
| Coding | C | Runs well | 31.3 tok/s | 6178 ms | 171K |
| Agentic Coding | C | Runs well | 31.3 tok/s | 8987 ms | 171K |
| Reasoning | C | Runs well | 31.3 tok/s | 7302 ms | 171K |
| RAG | C | Runs well | 31.3 tok/s | 11233 ms | 171K |
Inference speed
Estimated decode speed (tokens/sec) for starcoder2 15b instruct v0.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 131.2 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 83.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 75.5 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 71.6 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 68.3 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 60.9 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 50.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 48.1 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 34.7 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 34.7 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 26.7 | Heavy offload |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 26.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 24.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 15.7 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 5.9 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C44 |
Q3_K_S | 3 | 7.4 GB | Low | C44 |
NVFP4 | 4 | 8.4 GB | Medium | C45 |
Q4_K_M | 4 | 9.2 GB | Medium | C45 |
Q5_K_M | 5 | 10.8 GB | High | C46 |
Q6_K | 6 | 12.3 GB | High | C47 |
Q8_0Best for your GPU | 8 | 16.1 GB | Very High | C49 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.
Run
lms load hf-bartowski--starcoder2-15b-instruct-v0-1-gguf && lms server startUpgrade options
~$2,499 MSRP
Raises estimated decode speed by about 62%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Radeon Pro W6800 32GB can run starcoder2 15b instruct v0.1 with a C grade (Runs well). Expected decode speed: 31.3 tok/s.
starcoder2 15b instruct v0.1 (15B parameters) requires approximately 15.0 GB of memory with Q4_K_M quantization.
The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.
On Radeon Pro W6800 32GB, starcoder2 15b instruct v0.1 achieves approximately 31.3 tokens per second decode speed with a time-to-first-token of 6178ms using Q4_K_M quantization.
For coding workloads, starcoder2 15b instruct v0.1 on Radeon Pro W6800 32GB receives a C grade with 31.3 tok/s and 171K context.
On Radeon Pro W6800 32GB, starcoder2 15b instruct v0.1 can safely use up to 171K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
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