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URL: https://willitrunai.com/can-run/hf-second-state--starcoder2-7b-gguf-on-rtx-4050-laptop-6gb

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Can StarCoder2 7B run on RTX 4050 Laptop 6GB?

BARELY — Tight on Memory

D39Poor
○Estimated from fit model

StarCoder2 7B needs ~6.6 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload
Share:

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) — 6.6 GB, 18.6 tok/s, Very compromised (needs ~0.4 GB host RAM)
6.6 GB required6.0 GB available
110% VRAM needed

0.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.4 GB host RAM)

Decode

18.6 tok/s

TTFT

10415 ms

Safe context

4K

Memory

6.6 GB / 6.0 GB

Offload

10%

Memory breakdown

Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom0.6 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on RTX 4050 Laptop 6GB
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: 18.6 tok/s decode · 10.4s TTFT (warm) · 47 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload (needs ~0.1 GB host RAM)21.3 tok/s4962 ms4K
CodingDVery compromised (needs ~0.4 GB host RAM)18.6 tok/s10415 ms4K
Agentic CodingFToo heavy14.5 tok/s19392 ms4K
ReasoningDVery compromised (needs ~0.4 GB host RAM)18.6 tok/s12308 ms4K
RAGFToo heavy14.5 tok/s24240 ms

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC54
Q3_K_SBest for your GPU
3
3.4 GB
LowC54

Get started

Copy-paste commands to run StarCoder2 7B on your machine.

Run

lms load hf-second-state--starcoder2-7b-gguf && lms server start

Upgrade options

Hardware that runs StarCoder2 7B well

👁 NVIDIA
RTX 3050 8GBBudget pick
8 GB VRAM (+2)224 GB/s (+32)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.29.1 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 56%.

~$249 MSRP

👁 NVIDIA
RTX 5060 8GBBest value
8 GB VRAM (+2)448 GB/s (+256)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.64 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 244%.

~$299 MSRP

👁 NVIDIA
RTX 5050 8GBNVIDIA upgrade
8 GB VRAM (+2)224 GB/s (+32)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.44.1 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 137%.

~$299 MSRP

Frequently asked questions

See all results for RTX 4050 Laptop 6GBSee all hardware for StarCoder2 7B
4K
NVFP4
4
3.9 GB
Medium
F0
Q4_K_M
4
4.3 GB
MediumF0
Q5_K_M
5
5.0 GB
HighF0
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.