Can StarCoder2 7B run on GTX 1070 8GB?

YES — Runs Great

C53Usable

Estimated from fit model

StarCoder2 7B needs ~6.5 GB VRAM. GTX 1070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~39 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 6.5 GB, 38.6 tok/s, Runs well

6.5 GB required8.0 GB available

81% VRAM used

Fit status

Runs well

Decode

38.6 tok/s

TTFT

5014 ms

Safe context

16K

Memory

6.5 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on GTX 1070 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: 38.6 tok/s decode · 5.0s TTFT (warm) · 97 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	38.6 tok/s	2735 ms	16K
Coding	C	Runs well	38.6 tok/s	5014 ms	16K
Agentic Coding	C	Tight fit	38.6 tok/s	7293 ms	16K
Reasoning	C	Runs well	38.6 tok/s	5925 ms	16K
RAG	C	Tight fit	38.6 tok/s	9116 ms	16K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on GTX 1070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C52
Q3_K_S	3	3.4 GB	Low	C53
NVFP4	4	3.9 GB	Medium	C53
Q4_K_M	4	4.3 GB	Medium	C52
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 starcoder2-7b && lms server start

Upgrade-Optionen

Hardware, die StarCoder2 7B gut ausführt

RTX 5070 12GBGünstige Wahl

12 GB VRAM (+4)672 GB/s (+416)

Raises estimated decode speed by about 180%.108.2 tok/s Dekodierung

Raises estimated decode speed by about 180%.

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

ca. $549 MSRP

RTX 4070 Super 12GBBestes Preis-Leistungs-Verhältnis

12 GB VRAM (+4)504 GB/s (+248)

Raises estimated decode speed by about 137%.91.3 tok/s Dekodierung

Raises estimated decode speed by about 137%.

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

ca. $599 MSRP

RTX 4070 12GBNVIDIA-Upgrade

12 GB VRAM (+4)504 GB/s (+248)

Raises estimated decode speed by about 130%.88.9 tok/s Dekodierung

Raises estimated decode speed by about 130%.

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

ca. $599 MSRP

Frequently asked questions

Can GTX 1070 8GB run StarCoder2 7B?

Yes, GTX 1070 8GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 38.6 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 6.5 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 GTX 1070 8GB?

On GTX 1070 8GB, StarCoder2 7B achieves approximately 38.6 tokens per second decode speed with a time-to-first-token of 5014ms using Q4_K_M quantization.

Can GTX 1070 8GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on GTX 1070 8GB receives a C grade with 38.6 tok/s and 16K context.

What context window can StarCoder2 7B use on GTX 1070 8GB?

On GTX 1070 8GB, StarCoder2 7B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for GTX 1070 8GB See all hardware for StarCoder2 7B

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