Can speechless zephyr code functionary 7b run on RTX 4060 Ti 16GB?

YES — Runs Great

C50Usable

Estimated from fit model

speechless zephyr code functionary 7b needs ~7.9 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 7.9 GB, 49.2 tok/s, Runs well

7.9 GB required16.0 GB available

49% VRAM used

Fit status

Runs well

Decode

49.2 tok/s

TTFT

3932 ms

Safe context

174K

Memory

7.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsspeechless zephyr code functionary 7b on RTX 4060 Ti 16GB
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: 49.2 tok/s decode · 3.9s TTFT (warm) · 123 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	Runs well	49.2 tok/s	2145 ms	174K
Coding	C	Runs well	49.2 tok/s	3932 ms	174K
Agentic Coding	C	Runs well	49.2 tok/s	5719 ms	174K
Reasoning	C	Runs well	49.2 tok/s	4647 ms	174K
RAG	C	Runs well	49.2 tok/s	7149 ms	174K

Inference speed

speechless zephyr code functionary 7b inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for speechless zephyr code functionary 7b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	88.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	87.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	87.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	55.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	51.5	Fits
RTX 4060 8GB	8 GB	Q4_K_M	46.5	Tight
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	45.3	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How speechless zephyr code functionary 7b (7B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C47
Q3_K_S	3	3.4 GB	Low	C47
NVFP4	4	3.9 GB	Medium	C48
Q4_K_M	4	4.3 GB	Medium	C48
Q5_K_M	5	5.0 GB	High	C49
Q6_K	6	5.7 GB	High	C49
Q8_0Best for your GPU	8	7.5 GB	Very High	C51
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-Optionen

Hardware, die speechless zephyr code functionary 7b gut ausführt

RX 7900 XT 20GBBestes Preis-Leistungs-Verhältnis

20 GB VRAM (+4)800 GB/s (+512)

Raises estimated decode speed by about 99%.98 tok/s Dekodierung

Raises estimated decode speed by about 99%.

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

ca. $899 MSRP

RTX A4500 20GBGünstige Wahl

20 GB VRAM (+4)640 GB/s (+352)

Raises estimated decode speed by about 99%.98 tok/s Dekodierung

Raises estimated decode speed by about 99%.

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

ca. $2,000 MSRP

Frequently asked questions

Can RTX 4060 Ti 16GB run speechless zephyr code functionary 7b?

Yes, RTX 4060 Ti 16GB can run speechless zephyr code functionary 7b with a C grade (Runs well). Expected decode speed: 49.2 tok/s.

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

speechless zephyr code functionary 7b (7B parameters) requires approximately 7.9 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 4060 Ti 16GB?

On RTX 4060 Ti 16GB, speechless zephyr code functionary 7b achieves approximately 49.2 tokens per second decode speed with a time-to-first-token of 3932ms using Q4_K_M quantization.

Can RTX 4060 Ti 16GB run speechless zephyr code functionary 7b for coding?

For coding workloads, speechless zephyr code functionary 7b on RTX 4060 Ti 16GB receives a C grade with 49.2 tok/s and 174K context.

What context window can speechless zephyr code functionary 7b use on RTX 4060 Ti 16GB?

On RTX 4060 Ti 16GB, speechless zephyr code functionary 7b can safely use up to 174K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4060 Ti 16GB See all hardware for speechless zephyr code functionary 7b

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