MacBook Pro M3 Max 128GB经济之选
128 GB Unified (+48)
C
This setup is broadly balanced for this model.42 tok/s 解码
~$2,499 MSRP
Can StarCoder2 3B run on NVIDIA A100 80GB?
YES — Runs Great
C42Usable
Estimated from fit model
StarCoder2 3B needs ~11.4 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 11.4 GB, 42.0 tok/s, Runs well
11.4 GB required80.0 GB available
14% VRAM used
Fit status
Runs well
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
3.1M
Memory
11.4 GB / 80.0 GB
Memory breakdown
Weights1.8 GB
KV Cache0.4 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsStarCoder2 3B on NVIDIA A100 80GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]
The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
"""Sort a list using the quicksort algorithm.
Args:
arr: List of integers to sort.
Returns:
A new sorted list.
"""
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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 | 42.0 tok/s | 2514 ms | 3.1M |
| Coding | C | Runs well | 42.0 tok/s | 4610 ms | 3.1M |
| Agentic Coding | C | Runs well | 42.0 tok/s | 6705 ms | 3.1M |
| Reasoning | C | Runs well | 42.0 tok/s | 5448 ms | 3.1M |
| RAG | C | Runs well | 42.0 tok/s | 8381 ms | 3.1M |
Inference speed
StarCoder2 3B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for StarCoder2 3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~57 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 | 57.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 48.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 48.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 42.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 42.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 42.0 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 42.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 42.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 42.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 42.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 42.0 | Fits |
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.
Quantization options
How StarCoder2 3B (3B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | D39 |
Q3_K_S | 3 | 1.5 GB | Low | D39 |
NVFP4 | 4 | 1.7 GB | Medium | D39 |
Q4_K_M | 4 | 1.8 GB | Medium | D39 |
Q5_K_M | 5 | 2.2 GB | High | D39 |
Q6_K | 6 | 2.5 GB | High | D39 |
Q8_0 | 8 | 3.2 GB | Very High | D39 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | D40 |
Get started
Copy-paste commands to run StarCoder2 3B on your machine.
Run
lms load hf-second-state--starcoder2-3b-gguf && lms server start升级选项
能流畅运行 StarCoder2 3B 的硬件
Mac Studio M2 Ultra 128GB最佳性价比
128 GB Unified (+48)
C
This setup is broadly balanced for this model.42 tok/s 解码
~$3,999 MSRP
NVIDIA DGX Spark 128GBNVIDIA 升级128 GB Unified (+48)
C
Adds memory headroom for longer context windows and future model growth.42 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
Frequently asked questions
Can NVIDIA A100 80GB run StarCoder2 3B?
Yes, NVIDIA A100 80GB can run StarCoder2 3B with a C grade (Runs well). Expected decode speed: 42.0 tok/s.
How much VRAM does StarCoder2 3B need?
StarCoder2 3B (3B parameters) requires approximately 11.4 GB of memory with Q4_K_M quantization.
What is the best quantization for StarCoder2 3B?
The recommended quantization for StarCoder2 3B is Q4_K_M, which balances quality and memory efficiency.
What speed will StarCoder2 3B run at on NVIDIA A100 80GB?
On NVIDIA A100 80GB, StarCoder2 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
Can NVIDIA A100 80GB run StarCoder2 3B for coding?
For coding workloads, StarCoder2 3B on NVIDIA A100 80GB receives a C grade with 42.0 tok/s and 3.1M context.
What context window can StarCoder2 3B use on NVIDIA A100 80GB?
On NVIDIA A100 80GB, StarCoder2 3B can safely use up to 3.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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