Can Granite 4.1 30B run on NVIDIA A10 24GB?

YES — With Offload

A71Great

Estimated from fit model

Granite 4.1 30B needs ~25.5 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 25.5 GB, 18.1 tok/s, Runs with offload (needs ~1.1 GB host RAM)

25.5 GB required24.0 GB available

106% VRAM needed

1.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~1.1 GB host RAM)

Decode

18.1 tok/s

TTFT

10673 ms

Safe context

10K

Memory

25.5 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights18.3 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGranite 4.1 30B on NVIDIA A10 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: 18.1 tok/s decode · 10.7s TTFT (warm) · 45 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload	27.5 tok/s	3841 ms	10K
Coding	A	Runs with offload (needs ~1.1 GB host RAM)	18.1 tok/s	10673 ms	10K
Agentic Coding	F	Too heavy	13.4 tok/s	20955 ms	10K
Reasoning	A	Runs with offload (needs ~1.1 GB host RAM)	18.1 tok/s	12614 ms	10K
RAG	F	Too heavy	13.4 tok/s	26193 ms	10K

Inference speed

Granite 4.1 30B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite 4.1 30B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~71 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	70.5	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	35.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	35.3	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	32.7	Fits
RTX 4090 24GB	24 GB	Q4_K_M	29.0	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	27.3	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	26.8	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	25.8	Fits
RTX 3090 24GB	24 GB	Q4_K_M	24.8	Offloads
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	22.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	14.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	12.9	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	10.5	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.7	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.3	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

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 4.1 30B (30B params) fits at each quantization level on NVIDIA A10 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A83
Q3_K_S	3	14.7 GB	Low	A82
NVFP4	4	16.8 GB	Medium	A82
Q4_K_MBest for your GPU	4	18.3 GB	Medium	A82
Q5_K_M	5	21.6 GB	High	F0
Q6_K	6	24.6 GB	High	F0
Q8_0	8	32.1 GB	Very High	F0
F16	16	61.5 GB	Maximum	F0

Get started

Copy-paste commands to run Granite 4.1 30B on your machine.

Run

ollama run granite4.1:30b

Your hardware

More models your NVIDIA A10 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.8 tok/s
Qwen 3.6 35B A3B	35B	A	30.5 tok/s
Qwen 3.5 35B A3B	35B	A	40.6 tok/s
Qwen 3 32B	32B	A	15.6 tok/s
Qwen 3 30B A3B	30.5B	S	70.8 tok/s

Frequently asked questions

Can NVIDIA A10 24GB run Granite 4.1 30B?

Yes, NVIDIA A10 24GB can run Granite 4.1 30B with a A grade (Runs with offload (needs ~1.1 GB host RAM)). Expected decode speed: 18.1 tok/s.

How much VRAM does Granite 4.1 30B need?

Granite 4.1 30B (30B parameters) requires approximately 25.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 4.1 30B?

The recommended quantization for Granite 4.1 30B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 4.1 30B run at on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Granite 4.1 30B achieves approximately 18.1 tokens per second decode speed with a time-to-first-token of 10673ms using Q4_K_M quantization.

Can NVIDIA A10 24GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on NVIDIA A10 24GB receives a A grade with 18.1 tok/s and 10K context.

What context window can Granite 4.1 30B use on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Granite 4.1 30B can safely use up to 10K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Granite 4.1 30B feels slow on NVIDIA A10 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for NVIDIA A10 24GB See all hardware for Granite 4.1 30B

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Preview:

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs with offload

27.5 tok/s

3841 ms

10K

Coding

Runs with offload (needs ~1.1 GB host RAM)

18.1 tok/s

10673 ms

10K

Agentic Coding

Too heavy

13.4 tok/s

20955 ms

10K

Reasoning

Runs with offload (needs ~1.1 GB host RAM)

18.1 tok/s

12614 ms

10K

RAG

Too heavy

13.4 tok/s

26193 ms

10K

Granite 4.1 30B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

70.5

Tight

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

35.3

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

35.3

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

32.7

Fits

RTX 4090 24GB

24 GB

Q4_K_M

29.0

Offloads

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

27.3

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

26.8

Offloads

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

25.8

Fits

RTX 3090 24GB

24 GB

Q4_K_M

24.8

Offloads

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

22.2

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

14.1

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

12.9

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

10.5

Too big

RTX 4070 12GB

12 GB

Q4_K_M

3.7

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.3

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

Quantization options

How Granite 4.1 30B (30B params) fits at each quantization level on NVIDIA A10 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A83
Q3_K_S	3	14.7 GB	Low	A82
NVFP4	4	16.8 GB	Medium	A82
Q4_K_MBest for your GPU	4	18.3 GB	Medium	A82
Q5_K_M	5	21.6 GB	High	F0
Q6_K	6	24.6 GB	High	F0
Q8_0	8	32.1 GB	Very High	F0
F16	16	61.5 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

70.8 tok/s

Qwen 3.6 35B A3B

35B

30.5 tok/s

Qwen 3.5 35B A3B

35B

40.6 tok/s

Qwen 3 32B

32B

15.6 tok/s

Qwen 3 30B A3B

30.5B

70.8 tok/s

Frequently asked questions

Can NVIDIA A10 24GB run Granite 4.1 30B?

Yes, NVIDIA A10 24GB can run Granite 4.1 30B with a A grade (Runs with offload (needs ~1.1 GB host RAM)). Expected decode speed: 18.1 tok/s.

How much VRAM does Granite 4.1 30B need?

Granite 4.1 30B (30B parameters) requires approximately 25.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 4.1 30B?

The recommended quantization for Granite 4.1 30B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 4.1 30B run at on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Granite 4.1 30B achieves approximately 18.1 tokens per second decode speed with a time-to-first-token of 10673ms using Q4_K_M quantization.

Can NVIDIA A10 24GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on NVIDIA A10 24GB receives a A grade with 18.1 tok/s and 10K context.

What context window can Granite 4.1 30B use on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Granite 4.1 30B can safely use up to 10K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Granite 4.1 30B feels slow on NVIDIA A10 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.