Can speechless zephyr code functionary 7b run on RX 580 8GB?

YES — Tight Fit

C50Usable

Estimated from fit model

speechless zephyr code functionary 7b needs ~6.8 GB VRAM. RX 580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 6.8 GB, 25.8 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

25.8 tok/s

TTFT

7510 ms

Safe context

40K

Memory

6.8 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsspeechless zephyr code functionary 7b on RX 580 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: 25.8 tok/s decode · 7.5s TTFT (warm) · 64 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	25.8 tok/s	4096 ms	40K
Coding	C	Tight fit	25.8 tok/s	7510 ms	40K
Agentic Coding	C	Runs with offload	25.8 tok/s	10923 ms	40K
Reasoning	C	Tight fit	25.8 tok/s	8875 ms	40K
RAG	C	Runs with offload	25.8 tok/s	13654 ms	40K

Quantization options

How speechless zephyr code functionary 7b (7B params) fits at each quantization level on RX 580 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

RX 9060 XT 16GBBudget pick

16 GB VRAM (+8)320 GB/s (+64)

Raises estimated decode speed by about 83%.47.2 tok/s decode

Raises estimated decode speed by about 83%.

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

~$349 MSRP

RX 7700 XT 12GBBest value

12 GB VRAM (+4)432 GB/s (+176)

Raises estimated decode speed by about 135%.60.7 tok/s decode

Raises estimated decode speed by about 135%.

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

~$449 MSRP

RX 6700 XT 12GBAMD upgrade

12 GB VRAM (+4)384 GB/s (+128)

Raises estimated decode speed by about 81%.46.8 tok/s decode

Raises estimated decode speed by about 81%.

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

~$479 MSRP

Frequently asked questions

Can RX 580 8GB run speechless zephyr code functionary 7b?

Yes, RX 580 8GB can run speechless zephyr code functionary 7b with a C grade (Tight fit). Expected decode speed: 25.8 tok/s.

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

speechless zephyr code functionary 7b (7B parameters) requires approximately 6.8 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 RX 580 8GB?

On RX 580 8GB, speechless zephyr code functionary 7b achieves approximately 25.8 tokens per second decode speed with a time-to-first-token of 7510ms using Q4_K_M quantization.

Can RX 580 8GB run speechless zephyr code functionary 7b for coding?

For coding workloads, speechless zephyr code functionary 7b on RX 580 8GB receives a C grade with 25.8 tok/s and 40K context.

What context window can speechless zephyr code functionary 7b use on RX 580 8GB?

On RX 580 8GB, speechless zephyr code functionary 7b can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 580 8GB See all hardware for speechless zephyr code functionary 7b

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<iframe src="https://willitrunai.com/embed/hf-uukuguy--speechless-zephyr-code-functionary-7b-on-rx-580-8gb" 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

Runs well

25.8 tok/s

4096 ms

40K

Coding

Tight fit

25.8 tok/s

7510 ms

40K

Agentic Coding

Runs with offload

25.8 tok/s

10923 ms

40K

Reasoning

Tight fit

25.8 tok/s

8875 ms

40K

RAG

Runs with offload

25.8 tok/s

13654 ms

40K

Quantization options

How speechless zephyr code functionary 7b (7B params) fits at each quantization level on RX 580 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