Can Granite Code 3B run on NVIDIA A100 80GB?

YES — Runs Great

B59Good

Estimated from fit model

Granite Code 3B needs ~13.5 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 13.5 GB, 42.0 tok/s, Runs well

13.5 GB required80.0 GB available

17% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

Memory

13.5 GB / 80.0 GB

Memory breakdown

Weights1.8 GB

KV Cache2.5 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsGranite Code 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	B	Runs well	42.0 tok/s	2514 ms	8K
Coding	B	Runs well	42.0 tok/s	4610 ms	8K
Agentic Coding	B	Runs well	42.0 tok/s	6705 ms	8K
Reasoning	B	Runs well	42.0 tok/s	5448 ms	8K
RAG	B	Runs well	42.0 tok/s	8381 ms	8K

Inference speed

Granite Code 3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite Code 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 4060 8GB	8 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
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 Granite Code 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	B56
Q3_K_S	3	1.5 GB	Low	B56
NVFP4	4	1.7 GB	Medium	B56
Q4_K_M	4	1.8 GB	Medium	B56
Q5_K_M	5	2.2 GB	High	B56
Q6_K	6	2.5 GB	High	B56
Q8_0	8	3.2 GB	Very High	B56
F16Best for your GPU	16	6.1 GB	Maximum	B56

Get started

Copy-paste commands to run Granite Code 3B on your machine.

Run

ollama run granite-code:3b

Upgrade options

Hardware that runs Granite Code 3B well

MacBook Pro M3 Max 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.42 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value

128 GB Unified (+48)

This setup is broadly balanced for this model.42 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.42 tok/s decode

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

Frequently asked questions

Can NVIDIA A100 80GB run Granite Code 3B?

Yes, NVIDIA A100 80GB can run Granite Code 3B with a B grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does Granite Code 3B need?

Granite Code 3B (3B parameters) requires approximately 13.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 3B?

The recommended quantization for Granite Code 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 3B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Granite Code 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 Granite Code 3B for coding?

For coding workloads, Granite Code 3B on NVIDIA A100 80GB receives a B grade with 42.0 tok/s and 8K context.

What context window can Granite Code 3B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Granite Code 3B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Granite Code 3B

Embed this result▼

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

42.0 tok/s

2514 ms

Coding

Runs well

42.0 tok/s

4610 ms

Agentic Coding

Runs well

42.0 tok/s

6705 ms

Reasoning

Runs well

42.0 tok/s

5448 ms

RAG

Runs well

42.0 tok/s

8381 ms

Granite Code 3B inference speed — tokens per second by GPU & Mac

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 4060 8GB

8 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

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

Quantization options

How Granite Code 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	B56
Q3_K_S	3	1.5 GB	Low	B56
NVFP4	4	1.7 GB	Medium	B56
Q4_K_M	4	1.8 GB	Medium	B56
Q5_K_M	5	2.2 GB	High	B56
Q6_K	6	2.5 GB	High	B56
Q8_0	8	3.2 GB	Very High	B56
F16Best for your GPU	16	6.1 GB	Maximum	B56

Frequently asked questions

Can NVIDIA A100 80GB run Granite Code 3B?

Yes, NVIDIA A100 80GB can run Granite Code 3B with a B grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does Granite Code 3B need?

Granite Code 3B (3B parameters) requires approximately 13.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 3B?

The recommended quantization for Granite Code 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 3B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Granite Code 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 Granite Code 3B for coding?

For coding workloads, Granite Code 3B on NVIDIA A100 80GB receives a B grade with 42.0 tok/s and 8K context.

What context window can Granite Code 3B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Granite Code 3B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.