RTX 5090 32GB经济之选32 GB VRAM (+8)1792 GB/s (+1446)
C
Raises estimated decode speed by about 88%.98 tok/s 解码
Raises estimated decode speed by about 88%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can StarCoder2 7B run on Tesla P40 24GB?
YES — Runs Great
C47Usable
Estimated from fit model
StarCoder2 7B needs ~8.4 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 8.4 GB, 52.2 tok/s, Runs well
8.4 GB required24.0 GB available
35% VRAM used
Fit status
Runs well
Decode
52.2 tok/s
TTFT
3709 ms
Safe context
16K
Memory
8.4 GB / 24.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.5 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsStarCoder2 7B on Tesla P40 24GB
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: 52.2 tok/s decode · 3.7s TTFT (warm) · 131 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 52.2 tok/s | 2023 ms | 16K |
| Coding | C | Runs well | 52.2 tok/s | 3709 ms | 16K |
| Agentic Coding | C | Runs well | 52.2 tok/s | 5396 ms | 16K |
| Reasoning | C | Runs well | 52.2 tok/s | 4384 ms | 16K |
| RAG | C | Runs well | 52.2 tok/s | 6745 ms | 16K |
Inference speed
StarCoder2 7B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for StarCoder2 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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 | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 96.6 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 95.9 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 95.9 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 61.4 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 60.7 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 56.2 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 49.4 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 46.7 | 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 7B (7B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C44 |
Q3_K_S | 3 | 3.4 GB | Low | C44 |
NVFP4 | 4 | 3.9 GB | Medium | C44 |
Q4_K_M | 4 | 4.3 GB | Medium | C44 |
Q5_K_M | 5 | 5.0 GB | High | C45 |
Q6_K | 6 | 5.7 GB | High | C45 |
Q8_0 | 8 | 7.5 GB | Very High | C46 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C49 |
Get started
Copy-paste commands to run StarCoder2 7B on your machine.
Run
lms load starcoder2-7b && lms server start升级选项
能流畅运行 StarCoder2 7B 的硬件
RTX 5090 32GB经济之选RTX PRO 4500 Blackwell 32GB最佳性价比32 GB VRAM (+8)896 GB/s (+550)
C
Raises estimated decode speed by about 88%.98 tok/s 解码
Raises estimated decode speed by about 88%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA 升级32 GB VRAM (+8)576 GB/s (+230)
C
Raises estimated decode speed by about 88%.98 tok/s 解码
Raises estimated decode speed by about 88%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Frequently asked questions
Can Tesla P40 24GB run StarCoder2 7B?
Yes, Tesla P40 24GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 52.2 tok/s.
How much VRAM does StarCoder2 7B need?
StarCoder2 7B (7B parameters) requires approximately 8.4 GB of memory with Q4_K_M quantization.
What is the best quantization for StarCoder2 7B?
The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will StarCoder2 7B run at on Tesla P40 24GB?
On Tesla P40 24GB, StarCoder2 7B achieves approximately 52.2 tokens per second decode speed with a time-to-first-token of 3709ms using Q4_K_M quantization.
Can Tesla P40 24GB run StarCoder2 7B for coding?
For coding workloads, StarCoder2 7B on Tesla P40 24GB receives a C grade with 52.2 tok/s and 16K context.
What context window can StarCoder2 7B use on Tesla P40 24GB?
On Tesla P40 24GB, StarCoder2 7B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
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