Can Qwen 2.5 Coder 0.5B run on NVIDIA A100 80GB?

Q: What should I upgrade first if Qwen 2.5 Coder 0.5B feels slow on NVIDIA A100 80GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — Runs Great

C45Usable

Estimated from fit model

Qwen 2.5 Coder 0.5B needs ~9.7 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~7 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Memory bandwidth

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

Q4_K_M (Medium quality) — 9.7 GB, 7.0 tok/s, Runs well

9.7 GB required80.0 GB available

12% VRAM used

Fit status

Runs well

Decode

7.0 tok/s

TTFT

27657 ms

Safe context

131K

Memory

9.7 GB / 80.0 GB

Memory breakdown

Weights0.3 GB

KV Cache0.2 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 0.5B 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: 7.0 tok/s decode · 27.7s TTFT (warm) · 18 tok/s prefill

What limits this setup

This model fits, but memory bandwidth is the part holding decode speed back.

Throughput will feel slow

Estimated decode speed is only 7.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

Best improvement path

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	7.0 tok/s	15086 ms	131K
Coding	C	Runs well	7.0 tok/s	27657 ms	131K
Agentic Coding	C	Runs well	7.0 tok/s	40229 ms	131K
Reasoning	C	Runs well	7.0 tok/s	32686 ms	131K
RAG	C	Runs well	7.0 tok/s	50286 ms	131K

Inference speed

Qwen 2.5 Coder 0.5B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 Coder 0.5B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~10 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	9.5	Fits
RTX 4090 24GB	24 GB	Q4_K_M	8.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	7.0

Quantization options

How Qwen 2.5 Coder 0.5B (0.5B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.2 GB	Low	C49
Q3_K_S	3	0.2 GB	Low	C49
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 Coder 0.5B on your machine.

Run

ollama run qwen2.5-coder:0.5b

Upgrade options

Hardware that runs Qwen 2.5 Coder 0.5B well

MacBook Pro M3 Max 128GBBudget pick

128 GB Unified (+48)

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.7 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value

128 GB Unified (+48)

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.7 tok/s decode

~$3,999 MSRP

NVIDIA DGX Spark 128GBNVIDIA upgrade

128 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.7 tok/s decode

Adds memory headroom for longer context windows and future model growth.

Frequently asked questions

Can NVIDIA A100 80GB run Qwen 2.5 Coder 0.5B?

Yes, NVIDIA A100 80GB can run Qwen 2.5 Coder 0.5B with a C grade (Runs well). Expected decode speed: 7.0 tok/s.

How much VRAM does Qwen 2.5 Coder 0.5B need?

Qwen 2.5 Coder 0.5B (0.5B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 0.5B?

The recommended quantization for Qwen 2.5 Coder 0.5B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 0.5B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 2.5 Coder 0.5B achieves approximately 7.0 tokens per second decode speed with a time-to-first-token of 27657ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Qwen 2.5 Coder 0.5B for coding?

For coding workloads, Qwen 2.5 Coder 0.5B on NVIDIA A100 80GB receives a C grade with 7.0 tok/s and 131K context.

What context window can Qwen 2.5 Coder 0.5B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 2.5 Coder 0.5B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 2.5 Coder 0.5B feels slow on NVIDIA A100 80GB?

See all results for NVIDIA A100 80GB See all hardware for Qwen 2.5 Coder 0.5B

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