Can StarCoder2 15B run on NVIDIA H100 80GB?

YES — Runs Great

C49Usable

Estimated from fit model

StarCoder2 15B needs ~21.2 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q5_K_M quantization, expect ~210 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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 21.2 GB, 210.0 tok/s, Runs well

21.2 GB required80.0 GB available

27% VRAM used

Fit status

Runs well

Decode

210.0 tok/s

TTFT

922 ms

Safe context

16K

Memory

21.2 GB / 80.0 GB

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on NVIDIA H100 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: 210.0 tok/s decode · 922ms TTFT (warm) · 525 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	210.0 tok/s	503 ms	16K
Coding	C	Runs well	210.0 tok/s	922 ms	16K
Agentic Coding	C	Runs well	210.0 tok/s	1341 ms	16K
Reasoning	C	Runs well	210.0 tok/s	1090 ms	16K
RAG	C	Runs well	210.0 tok/s	1676 ms	16K

Inference speed

StarCoder2 15B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder2 15B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~124 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	Q5_K_M	123.8	Fits
RTX 4090 24GB	24 GB	Q5_K_M	79.0	Fits
RX 7900 XTX 24GB	24 GB	Q5_K_M	71.3	Fits
RTX 3090 24GB	24 GB	Q5_K_M	67.6	Fits
RTX 4080 Super 16GB	16 GB	Q5_K_M	64.4	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	57.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	47.8	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	45.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	32.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	32.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	24.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	22.7	Fits
RTX 4070 12GB	12 GB	Q5_K_M	21.2	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	20.0	Fits
RTX 3060 12GB	12 GB	Q5_K_M	12.5	Heavy offload
RTX 4060 8GB	8 GB	Q5_K_M	4.7	Too big

Estimates for single-stream decoding at Q5_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 15B (15B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C41
Q3_K_S	3	7.4 GB	Low	C41
NVFP4	4	8.4 GB	Medium	C41
Q4_K_M	4	9.2 GB	Medium	C41
Q5_K_M	5	10.8 GB	High	C42
Q6_K	6	12.3 GB	High	C42
Q8_0	8	16.1 GB	Very High	C42
F16Best for your GPU	16	30.7 GB	Maximum	C45

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bigcode/starcoder2-15b" \
  --hf-file "starcoder2-15b-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can NVIDIA H100 80GB run StarCoder2 15B?

Yes, NVIDIA H100 80GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 210.0 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 21.2 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, StarCoder2 15B achieves approximately 210.0 tokens per second decode speed with a time-to-first-token of 922ms using Q5_K_M quantization.

Can NVIDIA H100 80GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on NVIDIA H100 80GB receives a C grade with 210.0 tok/s and 16K context.

What context window can StarCoder2 15B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, StarCoder2 15B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GB See all hardware for StarCoder2 15B

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