Can speechless zephyr code functionary 7b run on Radeon RX 7800M 12GB?

YES — Runs Great

C53Usable

Estimated from fit model

speechless zephyr code functionary 7b needs ~7.2 GB VRAM. Radeon RX 7800M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~60 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) — 7.2 GB, 59.7 tok/s, Runs well

7.2 GB required12.0 GB available

60% VRAM used

Fit status

Runs well

Decode

59.7 tok/s

TTFT

3243 ms

Safe context

110K

Memory

7.2 GB / 12.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsspeechless zephyr code functionary 7b on Radeon RX 7800M 12GB
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: 59.7 tok/s decode · 3.2s TTFT (warm) · 149 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	59.7 tok/s	1769 ms	110K
Coding	C	Runs well	59.7 tok/s	3243 ms	110K
Agentic Coding	C	Runs well	59.7 tok/s	4718 ms	110K
Reasoning	C	Runs well	59.7 tok/s	3833 ms	110K
RAG	C	Runs well	59.7 tok/s	5897 ms	110K

Quantization options

How speechless zephyr code functionary 7b (7B params) fits at each quantization level on Radeon RX 7800M 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C49
Q3_K_S	3	3.4 GB	Low	C50
NVFP4	4	3.9 GB	Medium	C50
Q4_K_M	4	4.3 GB	Medium	C51
Q5_K_M	5	5.0 GB	High	C52
Q6_K	6	5.7 GB	High	C52
Q8_0Best for your GPU	8	7.5 GB	Very High	C52
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

Frequently asked questions

Can Radeon RX 7800M 12GB run speechless zephyr code functionary 7b?

Yes, Radeon RX 7800M 12GB can run speechless zephyr code functionary 7b with a C grade (Runs well). Expected decode speed: 59.7 tok/s.

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

speechless zephyr code functionary 7b (7B parameters) requires approximately 7.2 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 Radeon RX 7800M 12GB?

On Radeon RX 7800M 12GB, speechless zephyr code functionary 7b achieves approximately 59.7 tokens per second decode speed with a time-to-first-token of 3243ms using Q4_K_M quantization.

Can Radeon RX 7800M 12GB run speechless zephyr code functionary 7b for coding?

For coding workloads, speechless zephyr code functionary 7b on Radeon RX 7800M 12GB receives a C grade with 59.7 tok/s and 110K context.

What context window can speechless zephyr code functionary 7b use on Radeon RX 7800M 12GB?

On Radeon RX 7800M 12GB, speechless zephyr code functionary 7b can safely use up to 110K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon RX 7800M 12GB See all hardware for speechless zephyr code functionary 7b

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

59.7 tok/s

1769 ms

110K

Coding

Runs well

59.7 tok/s

3243 ms

110K

Agentic Coding

Runs well

59.7 tok/s

4718 ms

110K

Reasoning

Runs well

59.7 tok/s

3833 ms

110K

RAG

Runs well

59.7 tok/s

5897 ms

110K

Quantization options

How speechless zephyr code functionary 7b (7B params) fits at each quantization level on Radeon RX 7800M 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C49
Q3_K_S	3	3.4 GB	Low	C50
NVFP4	4	3.9 GB	Medium	C50
Q4_K_M	4	4.3 GB	Medium	C51
Q5_K_M	5	5.0 GB	High	C52
Q6_K	6	5.7 GB	High	C52
Q8_0Best for your GPU	8	7.5 GB	Very High	C52
F16	16	14.3 GB	Maximum	F0