Will It Run AI

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
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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) — 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

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit34.6 tok/s3050 ms34K
CodingCTight fit34.6 tok/s5592 ms34K
Agentic CodingCRuns with offload34.6 tok/s8133 ms34K
ReasoningCTight fit34.6 tok/s6608 ms34K
RAGCRuns with offload34.6 tok/s10167 ms34K

Quantization options

How speechless zephyr code functionary 7b (7B params) fits at each quantization level on RTX 3050 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC53
Q3_K_S
3
3.4 GB
LowC53
NVFP4
4
3.9 GB
MediumC53
Q4_K_M
4
4.3 GB
MediumC53
Q5_K_MBest for your GPU
5
5.0 GB
HighC53
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

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-Optionen

Hardware, die speechless zephyr code functionary 7b gut ausführt

NVIDIARTX 3060 12GBGünstige Wahl
12 GB VRAM (+4)360 GB/s (+136)
C
Raises estimated decode speed by about 61%.55.6 tok/s Dekodierung

Raises estimated decode speed by about 61%.

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

ca. $329 MSRP

NVIDIARTX 5060 Ti 16GBBestes Preis-Leistungs-Verhältnis
16 GB VRAM (+8)448 GB/s (+224)
C
Raises estimated decode speed by about 88%.65 tok/s Dekodierung

Raises estimated decode speed by about 88%.

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

ca. $449 MSRP

NVIDIARTX 5070 12GBNVIDIA-Upgrade
12 GB VRAM (+4)672 GB/s (+448)
B
Raises estimated decode speed by about 183%.98 tok/s Dekodierung

Raises estimated decode speed by about 183%.

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

ca. $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 8GBSee all hardware for speechless zephyr code functionary 7b
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