Can SQLCoder 7B run on RTX 5000 Ada Laptop 16GB?

YES — Runs Great

A84Great

Estimated from fit model

SQLCoder 7B needs ~9.0 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 9.0 GB, 98.0 tok/s, Runs well

9.0 GB required16.0 GB available

56% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

Memory

9.0 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsSQLCoder 7B on RTX 5000 Ada Laptop 16GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	A	Runs well	98.0 tok/s	1078 ms	8K
Coding	A	Runs well	98.0 tok/s	1976 ms	8K
Agentic Coding	S	Runs well	98.0 tok/s	2873 ms	8K
Reasoning	A	Runs well	98.0 tok/s	2335 ms	8K
RAG	S	Runs well	98.0 tok/s	3592 ms	8K

Quantization options

How SQLCoder 7B (7B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A76
Q3_K_S	3	3.4 GB	Low	A77
NVFP4	4	3.9 GB	Medium	A77
Q4_K_M	4	4.3 GB	Medium	A78
Q5_K_M	5	5.0 GB	High	A78
Q6_K	6	5.7 GB	High	A79
Q8_0Best for your GPU	8	7.5 GB	Very High	A81
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 5000 Ada Laptop 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	82.3 tok/s
Qwen 3 14B	14B	S	53.2 tok/s
Qwen 3 8B	8B	S	92.6 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	50.4 tok/s
GPT-OSS 20B	21B	A	47 tok/s

Frequently asked questions

Can RTX 5000 Ada Laptop 16GB run SQLCoder 7B?

Yes, RTX 5000 Ada Laptop 16GB can run SQLCoder 7B with a A grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does SQLCoder 7B need?

SQLCoder 7B (7B parameters) requires approximately 9.0 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 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, SQLCoder 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run SQLCoder 7B for coding?

For coding workloads, SQLCoder 7B on RTX 5000 Ada Laptop 16GB receives a A grade with 98.0 tok/s and 8K context.

What context window can SQLCoder 7B use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, 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.

See all results for RTX 5000 Ada Laptop 16GB See all hardware for SQLCoder 7B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/sqlcoder-7b-on-rtx-5000-ada-laptop-16gb" 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

Runs well

98.0 tok/s

1078 ms

Coding

Runs well

98.0 tok/s

1976 ms

Agentic Coding

Runs well

98.0 tok/s

2873 ms

Reasoning

Runs well

98.0 tok/s

2335 ms

RAG

Runs well

98.0 tok/s

3592 ms

Quantization options

How SQLCoder 7B (7B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A76
Q3_K_S	3	3.4 GB	Low	A77
NVFP4	4	3.9 GB	Medium	A77
Q4_K_M	4	4.3 GB	Medium	A78
Q5_K_M	5	5.0 GB	High	A78
Q6_K	6	5.7 GB	High	A79
Q8_0Best for your GPU	8	7.5 GB	Very High	A81
F16	16	14.3 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

82.3 tok/s

Qwen 3 14B

14B

53.2 tok/s

Qwen 3 8B

92.6 tok/s

Phi-4-reasoning-plus 14B

14.7B

50.4 tok/s

GPT-OSS 20B

21B

47 tok/s

Frequently asked questions

Can RTX 5000 Ada Laptop 16GB run SQLCoder 7B?

Yes, RTX 5000 Ada Laptop 16GB can run SQLCoder 7B with a A grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does SQLCoder 7B need?

SQLCoder 7B (7B parameters) requires approximately 9.0 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 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, SQLCoder 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run SQLCoder 7B for coding?

For coding workloads, SQLCoder 7B on RTX 5000 Ada Laptop 16GB receives a A grade with 98.0 tok/s and 8K context.

What context window can SQLCoder 7B use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, 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.