Can GPT-OSS 120B run on Intel Arc Pro A40 6GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

GPT-OSS 120B needs ~77.8 GB but Intel Arc Pro A40 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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) — 77.8 GB, exceeds 6.0 GB available

77.8 GB required6.0 GB available

1297% VRAM needed

71.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

77.8 GB / 6.0 GB

Offload

90%

Memory breakdown

Weights71.4 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGPT-OSS 120B on Intel Arc Pro A40 6GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 77.8 GB, but this setup only exposes 6.0 GB of usable VRAM.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Inference speed

GPT-OSS 120B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for GPT-OSS 120B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~9 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?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	9.2	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	8.5	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	7.1	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	6.7	Offloads
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	4.4	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	4.3	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	3.1	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.6	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.6	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.3	Too big
RTX 5090 32GB	32 GB	Q4_K_M	2.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 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 GPT-OSS 120B (117B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	45.6 GB	Low	F0
Q3_K_S	3	57.3 GB	Low	F0
NVFP4	4	65.5 GB	Medium	F0
Q4_K_M	4	71.4 GB	Medium	F0
Q5_K_M	5	84.2 GB	High	F0
Q6_K	6	95.9 GB	High	F0
Q8_0	8	125.2 GB	Very High	F0
F16	16	239.8 GB	Maximum	F0

Upgrade options

Hardware that runs GPT-OSS 120B well

Gaudi 3 128GBBudget pick

128 GB VRAM (+122)3700 GB/s (+3508)

Makes the model fit on the accelerator instead of staying completely out of reach.39.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Intel Data Center GPU Max 1550 128GBBest value

128 GB VRAM (+122)3200 GB/s (+3008)

Makes the model fit on the accelerator instead of staying completely out of reach.30.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc Pro A40 6GB run GPT-OSS 120B?

No, GPT-OSS 120B requires more memory than Intel Arc Pro A40 6GB provides.

How much VRAM does GPT-OSS 120B need?

GPT-OSS 120B (117B parameters) requires approximately 77.8 GB of memory with Q4_K_M quantization.

What is the best quantization for GPT-OSS 120B?

The recommended quantization for GPT-OSS 120B is Q4_K_M, which balances quality and memory efficiency.

What speed will GPT-OSS 120B run at on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, GPT-OSS 120B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can Intel Arc Pro A40 6GB run GPT-OSS 120B for coding?

For coding workloads, GPT-OSS 120B on Intel Arc Pro A40 6GB receives a F grade with 2.0 tok/s and 4K context.

What context window can GPT-OSS 120B use on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, GPT-OSS 120B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if GPT-OSS 120B feels slow on Intel Arc Pro A40 6GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Would CUDA be a better path than Intel Arc Pro A40 6GB for GPT-OSS 120B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc Pro A40 6GB See all hardware for GPT-OSS 120B

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Preview:

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 77.8 GB, but this setup only exposes 6.0 GB of usable VRAM.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

2.0 tok/s

52800 ms

Coding

Too heavy

2.0 tok/s

96800 ms

Agentic Coding

Too heavy

2.0 tok/s

140800 ms

Reasoning

Too heavy

2.0 tok/s

114400 ms

RAG

Too heavy

2.0 tok/s

176000 ms

GPT-OSS 120B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

9.2

Offloads

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

8.5

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

7.1

Offloads

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

6.7

Offloads

2× RX 7900 XTX 24GB

48 GB

Q4_K_M

4.4

Too big

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

4.3

Too big

2× RTX 4090 24GB

48 GB

Q4_K_M

3.1

Too big

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

2.6

Too big

2× RTX 3090 24GB

48 GB

Q4_K_M

2.6

Too big

4× RTX 3060 12GB

48 GB

Q4_K_M

2.3

Too big

RTX 5090 32GB

32 GB

Q4_K_M

2.0

Too big

RTX 4090 24GB

24 GB

Q4_K_M

2.0

Too big

RTX 4080 Super 16GB

16 GB

Q4_K_M

2.0

Too big

RTX 3090 24GB

24 GB

Q4_K_M

2.0

Too big

RTX 4070 12GB

12 GB

Q4_K_M

2.0

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.0

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

RX 7900 XTX 24GB

24 GB

Q4_K_M

2.0

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

2.0

Too big

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

2.0

Too big

Quant

Bits

VRAM

Quality

Fit

Q2_K

45.6 GB

Low

Q3_K_S

57.3 GB

Low

NVFP4

65.5 GB

Medium

Q4_K_M

71.4 GB

Medium

Q5_K_M

84.2 GB

High

Q6_K

95.9 GB

High

Q8_0

125.2 GB

Very High

F16

239.8 GB

Maximum

Frequently asked questions

Can Intel Arc Pro A40 6GB run GPT-OSS 120B?

No, GPT-OSS 120B requires more memory than Intel Arc Pro A40 6GB provides.

How much VRAM does GPT-OSS 120B need?

GPT-OSS 120B (117B parameters) requires approximately 77.8 GB of memory with Q4_K_M quantization.

What is the best quantization for GPT-OSS 120B?

The recommended quantization for GPT-OSS 120B is Q4_K_M, which balances quality and memory efficiency.

What speed will GPT-OSS 120B run at on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, GPT-OSS 120B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can Intel Arc Pro A40 6GB run GPT-OSS 120B for coding?

For coding workloads, GPT-OSS 120B on Intel Arc Pro A40 6GB receives a F grade with 2.0 tok/s and 4K context.

What context window can GPT-OSS 120B use on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, GPT-OSS 120B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if GPT-OSS 120B feels slow on Intel Arc Pro A40 6GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Would CUDA be a better path than Intel Arc Pro A40 6GB for GPT-OSS 120B?