Can speechless zephyr code functionary 7b run on RTX 3050 8GB?

YES — Tight Fit

C51Usable

Estimated from fit model

speechless zephyr code functionary 7b needs ~7.1 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: OllamaCapacity: TightBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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, 34.6 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

34.6 tok/s

TTFT

5592 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 feelsspeechless zephyr code functionary 7b on RTX 3050 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: 34.6 tok/s decode · 5.6s TTFT (warm) · 87 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	34.6 tok/s	3050 ms	34K
Coding	C	Tight fit	34.6 tok/s	5592 ms	34K
Agentic Coding	C	Runs with offload	34.6 tok/s	8133 ms	34K
Reasoning	C	Tight fit	34.6 tok/s	6608 ms	34K
RAG	C	Runs with offload	34.6 tok/s	10167 ms	34K

Quantization options

How speechless zephyr code functionary 7b (7B params) fits at each quantization level on RTX 3050 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	C53
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 speechless zephyr code functionary 7b on your machine.

Run

lms load hf-uukuguy--speechless-zephyr-code-functionary-7b && lms server start

Upgrade options

Hardware that runs speechless zephyr code functionary 7b well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+136)

Raises estimated decode speed by about 61%.55.6 tok/s decode

Raises estimated decode speed by about 61%.

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

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)448 GB/s (+224)

Raises estimated decode speed by about 88%.65 tok/s decode

Raises estimated decode speed by about 88%.

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

~$449 MSRP

RTX 5070 12GBNVIDIA upgrade

12 GB VRAM (+4)672 GB/s (+448)

Raises estimated decode speed by about 183%.98 tok/s decode

Raises estimated decode speed by about 183%.

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

~$549 MSRP

Frequently asked questions

Can RTX 3050 8GB run speechless zephyr code functionary 7b?

Yes, RTX 3050 8GB can run speechless zephyr code functionary 7b with a C grade (Tight fit). Expected decode speed: 34.6 tok/s.

How much VRAM does speechless zephyr code functionary 7b need?

speechless zephyr code functionary 7b (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.

What is the best quantization for speechless zephyr code functionary 7b?

The recommended quantization for speechless zephyr code functionary 7b is Q4_K_M, which balances quality and memory efficiency.

What speed will speechless zephyr code functionary 7b run at on RTX 3050 8GB?

On RTX 3050 8GB, speechless zephyr code functionary 7b achieves approximately 34.6 tokens per second decode speed with a time-to-first-token of 5592ms using Q4_K_M quantization.

Can RTX 3050 8GB run speechless zephyr code functionary 7b for coding?

For coding workloads, speechless zephyr code functionary 7b on RTX 3050 8GB receives a C grade with 34.6 tok/s and 34K context.

What context window can speechless zephyr code functionary 7b use on RTX 3050 8GB?

On RTX 3050 8GB, speechless zephyr code functionary 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 3050 8GB See all hardware for speechless zephyr code functionary 7b

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-uukuguy--speechless-zephyr-code-functionary-7b-on-rtx-3050-8gb" 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

34.6 tok/s

3050 ms

34K

Coding

Tight fit

34.6 tok/s

5592 ms

34K

Agentic Coding

Runs with offload

34.6 tok/s

8133 ms

34K

Reasoning

Tight fit

34.6 tok/s

6608 ms

34K

RAG

Runs with offload

34.6 tok/s

10167 ms

34K

Quantization options

How speechless zephyr code functionary 7b (7B params) fits at each quantization level on RTX 3050 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	C53
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0