Will It Run AI

Can SQLCoder 7B run on RTX 3500 Ada Laptop 12GB?

YES — Runs Great

S85Excellent
Estimated from fit model

SQLCoder 7B needs ~8.6 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~62 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
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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) 8.6 GB, 61.8 tok/s, Runs well
8.6 GB required12.0 GB available
72% VRAM used

Fit status

Runs well

Decode

61.8 tok/s

TTFT

3135 ms

Safe context

8K

Memory

8.6 GB / 12.0 GB

Memory breakdown

Weights4.3 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsSQLCoder 7B on RTX 3500 Ada Laptop 12GB
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: 61.8 tok/s decode · 3.1s TTFT (warm) · 154 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns well61.8 tok/s1710 ms8K
CodingSRuns well61.8 tok/s3135 ms8K
Agentic CodingATight fit61.8 tok/s4560 ms8K
ReasoningSRuns well61.8 tok/s3705 ms8K
RAGATight fit61.8 tok/s5700 ms8K

Quantization options

How SQLCoder 7B (7B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowA79
Q3_K_S
3
3.4 GB
LowA79
NVFP4
4
3.9 GB
MediumA80
Q4_K_M
4
4.3 GB
MediumA81
Q5_K_M
5
5.0 GB
HighA82
Q6_K
6
5.7 GB
HighA82
Q8_0Best for your GPU
8
7.5 GB
Very HighA81
F16
16
14.3 GB
MaximumF0

Get started

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

Run

ollama run sqlcoder

Your hardware

More models your RTX 3500 Ada Laptop 12GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen 3.5 9B9BS48 tok/s
AlibabaQwen 3 14B14BA18.5 tok/s
AlibabaQwen 3 8B8BS54 tok/s
NVIDIANemotron Nano 8B8BS54 tok/s
MistralMinistral 3 14B14BA18.4 tok/s

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run SQLCoder 7B?

Yes, RTX 3500 Ada Laptop 12GB can run SQLCoder 7B with a S grade (Runs well). Expected decode speed: 61.8 tok/s.

How much VRAM does SQLCoder 7B need?

SQLCoder 7B (7B parameters) requires approximately 8.6 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 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, SQLCoder 7B achieves approximately 61.8 tokens per second decode speed with a time-to-first-token of 3135ms using Q4_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run SQLCoder 7B for coding?

For coding workloads, SQLCoder 7B on RTX 3500 Ada Laptop 12GB receives a S grade with 61.8 tok/s and 8K context.

What context window can SQLCoder 7B use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, 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 3500 Ada Laptop 12GBSee all hardware for SQLCoder 7B
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