Can starcoder2 7b run on RTX 3070 8GB?

YES — Tight Fit

C53Usable

Estimated from fit model

starcoder2 7b needs ~7.1 GB VRAM. RTX 3070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~73 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: 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) — 7.1 GB, 73.4 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

73.4 tok/s

TTFT

2636 ms

Safe context

34K

Memory

7.1 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsstarcoder2 7b on RTX 3070 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: 73.4 tok/s decode · 2.6s TTFT (warm) · 184 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	C	Tight fit	73.4 tok/s	1438 ms	34K
Coding	C	Tight fit	73.4 tok/s	2636 ms	34K
Agentic Coding	C	Runs with offload	73.4 tok/s	3834 ms	34K
Reasoning	C	Tight fit	73.4 tok/s	3115 ms	34K
RAG	C	Runs with offload	73.4 tok/s	4793 ms	34K

Quantization options

How starcoder2 7b (7B params) fits at each quantization level on RTX 3070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_S	3	3.4 GB	Low	C53
NVFP4	4	3.9 GB	Medium	C53
Q4_K_M	4	4.3 GB	Medium	C53
Q5_K_MBest for your GPU	5	5.0 GB	High	C52
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 starcoder2 7b on your machine.

Run

lms load hf-quantfactory--starcoder2-7b-gguf && lms server start

Upgrade options

Hardware that runs starcoder2 7b well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.55.6 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+4)672 GB/s (+224)

Raises estimated decode speed by about 34%.98 tok/s decode

Raises estimated decode speed by about 34%.

Adds memory headroom for longer context windows and future model growth.

~$549 MSRP

RTX 4070 Super 12GBNVIDIA upgrade

12 GB VRAM (+4)504 GB/s (+56)

Adds memory headroom for longer context windows and future model growth.90.9 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$599 MSRP

Frequently asked questions

Can RTX 3070 8GB run starcoder2 7b?

Yes, RTX 3070 8GB can run starcoder2 7b with a C grade (Tight fit). Expected decode speed: 73.4 tok/s.

How much VRAM does starcoder2 7b need?

starcoder2 7b (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.

What is the best quantization for starcoder2 7b?

The recommended quantization for starcoder2 7b is Q4_K_M, which balances quality and memory efficiency.

What speed will starcoder2 7b run at on RTX 3070 8GB?

On RTX 3070 8GB, starcoder2 7b achieves approximately 73.4 tokens per second decode speed with a time-to-first-token of 2636ms using Q4_K_M quantization.

Can RTX 3070 8GB run starcoder2 7b for coding?

For coding workloads, starcoder2 7b on RTX 3070 8GB receives a C grade with 73.4 tok/s and 34K context.

What context window can starcoder2 7b use on RTX 3070 8GB?

On RTX 3070 8GB, starcoder2 7b can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3070 8GB See all hardware for starcoder2 7b

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-quantfactory--starcoder2-7b-gguf-on-rtx-3070-8gb" 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

73.4 tok/s

1438 ms

34K

Coding

Tight fit

73.4 tok/s

2636 ms

34K

Agentic Coding

Runs with offload

73.4 tok/s

3834 ms

34K

Reasoning

Tight fit

73.4 tok/s

3115 ms

34K

RAG

Runs with offload

73.4 tok/s

4793 ms

34K

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

C53

Q3_K_S

3.4 GB

Low

C53

NVFP4

3.9 GB

Medium

C53

Q4_K_M

4.3 GB

Medium

C53

Q5_K_MBest for your GPU

5.0 GB

High

C52

Q6_K

5.7 GB

High

Q8_0

7.5 GB

Very High

F16

14.3 GB

Maximum

Frequently asked questions

Can RTX 3070 8GB run starcoder2 7b?

Yes, RTX 3070 8GB can run starcoder2 7b with a C grade (Tight fit). Expected decode speed: 73.4 tok/s.

How much VRAM does starcoder2 7b need?

starcoder2 7b (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.

What is the best quantization for starcoder2 7b?

The recommended quantization for starcoder2 7b is Q4_K_M, which balances quality and memory efficiency.

What speed will starcoder2 7b run at on RTX 3070 8GB?

On RTX 3070 8GB, starcoder2 7b achieves approximately 73.4 tokens per second decode speed with a time-to-first-token of 2636ms using Q4_K_M quantization.

Can RTX 3070 8GB run starcoder2 7b for coding?

For coding workloads, starcoder2 7b on RTX 3070 8GB receives a C grade with 73.4 tok/s and 34K context.

What context window can starcoder2 7b use on RTX 3070 8GB?

On RTX 3070 8GB, starcoder2 7b can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.