Can SQLCoder 7B run on RTX 3060 Ti 8GB?

YES — With Offload

A82Great

Estimated from fit model

SQLCoder 7B needs ~7.9 GB VRAM. RTX 3060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~64 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 7.9 GB, 64.4 tok/s, Runs with offload

7.9 GB required8.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

64.4 tok/s

TTFT

3005 ms

Safe context

Memory

7.9 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsSQLCoder 7B on RTX 3060 Ti 8GB
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: 64.4 tok/s decode · 3.0s TTFT (warm) · 161 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

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	A	Tight fit	64.4 tok/s	1639 ms	8K
Coding	A	Runs with offload	64.4 tok/s	3005 ms	8K
Agentic Coding	F	Too heavy	31.0 tok/s	9081 ms	8K
Reasoning	A	Runs with offload	64.4 tok/s	3551 ms	8K
RAG	F	Too heavy	31.0 tok/s	11351 ms	8K

Inference speed

SQLCoder 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for SQLCoder 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	95.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	94.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	94.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	60.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	59.8	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	55.4	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	48.7	Fits
RTX 4060 8GB	8 GB	Q4_K_M	46.0	Offloads

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 SQLCoder 7B (7B params) fits at each quantization level on RTX 3060 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A83
Q3_K_S	3	3.4 GB	Low	A83
NVFP4	4	3.9 GB	Medium	A83
Q4_K_M	4	4.3 GB	Medium	A83
Q5_K_MBest for your GPU	5	5.0 GB	High	A82
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

Your hardware

More models your RTX 3060 Ti 8GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	A	27.4 tok/s
Qwen 3 8B	8B	A	29.9 tok/s
Nemotron Nano 8B	8B	A	36.9 tok/s
InternVL2 8B	8B	A	36.9 tok/s
Ministral 3 8B	8B	A	34.8 tok/s

Frequently asked questions

Can RTX 3060 Ti 8GB run SQLCoder 7B?

Yes, RTX 3060 Ti 8GB can run SQLCoder 7B with a A grade (Runs with offload). Expected decode speed: 64.4 tok/s.

How much VRAM does SQLCoder 7B need?

SQLCoder 7B (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for SQLCoder 7B?

The recommended quantization for SQLCoder 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will SQLCoder 7B run at on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, SQLCoder 7B achieves approximately 64.4 tokens per second decode speed with a time-to-first-token of 3005ms using Q4_K_M quantization.

Can RTX 3060 Ti 8GB run SQLCoder 7B for coding?

For coding workloads, SQLCoder 7B on RTX 3060 Ti 8GB receives a A grade with 64.4 tok/s and 8K context.

What context window can SQLCoder 7B use on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, SQLCoder 7B can safely use up to 8K tokens of context. 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 RTX 3060 Ti 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 3060 Ti 8GB See all hardware for SQLCoder 7B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/sqlcoder-7b-on-rtx-3060-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

64.4 tok/s

1639 ms

Coding

Runs with offload

64.4 tok/s

3005 ms

Agentic Coding

Too heavy

31.0 tok/s

9081 ms

Reasoning

Runs with offload

64.4 tok/s

3551 ms

RAG

Too heavy

31.0 tok/s

11351 ms

SQLCoder 7B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

98.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

98.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

98.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

98.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

98.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

98.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

98.0

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

98.0

Fits

RTX 4070 12GB

12 GB

Q4_K_M

95.2

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

94.4

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

94.4

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

60.4

Fits

RTX 3060 12GB

12 GB

Q4_K_M

59.8

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

55.4

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

48.7

Fits

RTX 4060 8GB

8 GB

Q4_K_M

46.0

Offloads

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

A83

Q3_K_S

3.4 GB

Low

A83

NVFP4

3.9 GB

Medium

A83

Q4_K_M

4.3 GB

Medium

A83

Q5_K_MBest for your GPU

5.0 GB

High

A82

Q6_K

5.7 GB

High

Q8_0

7.5 GB

Very High

F16

14.3 GB

Maximum

Model

Params

Grade

Decode

Capabilities

27.4 tok/s

29.9 tok/s

36.9 tok/s

36.9 tok/s

34.8 tok/s

Frequently asked questions

Can RTX 3060 Ti 8GB run SQLCoder 7B?

Yes, RTX 3060 Ti 8GB can run SQLCoder 7B with a A grade (Runs with offload). Expected decode speed: 64.4 tok/s.

How much VRAM does SQLCoder 7B need?

SQLCoder 7B (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for SQLCoder 7B?

The recommended quantization for SQLCoder 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will SQLCoder 7B run at on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, SQLCoder 7B achieves approximately 64.4 tokens per second decode speed with a time-to-first-token of 3005ms using Q4_K_M quantization.

Can RTX 3060 Ti 8GB run SQLCoder 7B for coding?

For coding workloads, SQLCoder 7B on RTX 3060 Ti 8GB receives a A grade with 64.4 tok/s and 8K context.

What context window can SQLCoder 7B use on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, SQLCoder 7B can safely use up to 8K tokens of context. 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 RTX 3060 Ti 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.