Can SQLCoder 7B run on Intel Arc Pro A40 6GB?

YES — With Q3_K_S

B68Good

Estimated from fit model

SQLCoder 7B needs ~6.9 GB VRAM. Intel Arc Pro A40 6GB has 6.0 GB. With Q3_K_S quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: 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.

SQLCoder 7B at Q4_K_M needs 7.7 GB — too much for Intel Arc Pro A40 6GB (6.0 GB). Runs at Q3_K_S (6.9 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.7 GB, exceeds 6.0 GB available

7.7 GB required6.0 GB available

128% VRAM needed

1.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.4 tok/s

TTFT

18542 ms

Safe context

Memory

7.7 GB / 6.0 GB

Offload

20%

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsSQLCoder 7B 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: 10.4 tok/s decode · 18.5s TTFT (warm) · 26 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.

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

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.

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.

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Very compromised (needs ~0.5 GB host RAM)	13.9 tok/s	7609 ms	4K
Coding	F	Too heavy	10.4 tok/s	18542 ms	4K
Agentic Coding	F	Too heavy	6.5 tok/s	43351 ms	4K
Reasoning	F	Too heavy	10.4 tok/s	21914 ms	4K
RAG	F	Too heavy	6.5 tok/s	54189 ms	4K

Quantization options

How SQLCoder 7B (7B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A84
Q3_K_SBest for your GPU	3	3.4 GB	Low	A83
NVFP4	4	3.9 GB	Medium	F0
Q4_K_M	4	4.3 GB	Medium	F0
Q5_K_M	5	5.0 GB	High	F0
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run SQLCoder 7B on your machine.

Run

ollama run sqlcoder

Upgrade options

Hardware that runs SQLCoder 7B well

Intel Arc A580 8GBBudget pick

8 GB VRAM (+2)512 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.63.2 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.

~$179 MSRP

Intel Arc B570 10GBBest value

10 GB VRAM (+4)380 GB/s (+188)

Makes the model fit on the accelerator instead of staying completely out of reach.51.7 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.

~$219 MSRP

Intel Arc B580 12GBIntel upgrade

12 GB VRAM (+6)456 GB/s (+264)

Makes the model fit on the accelerator instead of staying completely out of reach.55.1 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.

~$249 MSRP

Frequently asked questions

Can Intel Arc Pro A40 6GB run SQLCoder 7B?

Yes, Intel Arc Pro A40 6GB can run SQLCoder 7B at Q3_K_S quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 7.7 GB which exceeds available memory, but at Q3_K_S it needs only 6.9 GB. Expected decode speed: 15.4 tok/s.

How much VRAM does SQLCoder 7B need?

SQLCoder 7B (7B parameters) requires approximately 7.7 GB at Q4_K_M quantization. On Intel Arc Pro A40 6GB, it fits at Q3_K_S using 6.9 GB.

What is the best quantization for SQLCoder 7B?

The recommended quantization is Q4_K_M, but on Intel Arc Pro A40 6GB the best fitting quantization is Q3_K_S, which uses 6.9 GB.

What speed will SQLCoder 7B run at on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, SQLCoder 7B achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12568ms using Q3_K_S quantization.

Can Intel Arc Pro A40 6GB run SQLCoder 7B for coding?

For coding workloads, SQLCoder 7B on Intel Arc Pro A40 6GB receives a F grade with 10.4 tok/s and 4K context.

What context window can SQLCoder 7B use on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, SQLCoder 7B can safely use up to 8K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if SQLCoder 7B feels slow on Intel Arc Pro A40 6GB?

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.

Would CUDA be a better path than Intel Arc Pro A40 6GB for SQLCoder 7B?

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 SQLCoder 7B

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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.

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

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.

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.

Buy headroom, not only minimum fit

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

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Very compromised (needs ~0.5 GB host RAM)

13.9 tok/s

7609 ms

Coding

Too heavy

10.4 tok/s

18542 ms

Agentic Coding

Too heavy

6.5 tok/s

43351 ms

Reasoning

Too heavy

10.4 tok/s

21914 ms

RAG

Too heavy

6.5 tok/s

54189 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

A84

Q3_K_SBest for your GPU

3.4 GB

Low

A83

NVFP4

3.9 GB

Medium

Q4_K_M

4.3 GB

Medium

Q5_K_M

5.0 GB

High

Q6_K

5.7 GB

High

Q8_0

7.5 GB

Very High

F16

14.3 GB

Maximum

Frequently asked questions

Can Intel Arc Pro A40 6GB run SQLCoder 7B?

How much VRAM does SQLCoder 7B need?

SQLCoder 7B (7B parameters) requires approximately 7.7 GB at Q4_K_M quantization. On Intel Arc Pro A40 6GB, it fits at Q3_K_S using 6.9 GB.

What is the best quantization for SQLCoder 7B?

The recommended quantization is Q4_K_M, but on Intel Arc Pro A40 6GB the best fitting quantization is Q3_K_S, which uses 6.9 GB.

What speed will SQLCoder 7B run at on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, SQLCoder 7B achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12568ms using Q3_K_S quantization.

Can Intel Arc Pro A40 6GB run SQLCoder 7B for coding?

For coding workloads, SQLCoder 7B on Intel Arc Pro A40 6GB receives a F grade with 10.4 tok/s and 4K context.

What context window can SQLCoder 7B use on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, SQLCoder 7B can safely use up to 8K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if SQLCoder 7B feels slow on Intel Arc Pro A40 6GB?

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.

Would CUDA be a better path than Intel Arc Pro A40 6GB for SQLCoder 7B?