Will It Run AI

Can speechless zephyr code functionary 7b run on RX 6600 8GB?

YES — Tight Fit

C50Usable
Estimated from fit model

speechless zephyr code functionary 7b needs ~6.8 GB VRAM. RX 6600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: 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) — 6.8 GB, 25.7 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used

Fit status

Tight fit

Decode

25.7 tok/s

TTFT

7532 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 6600 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: 25.7 tok/s decode · 7.5s TTFT (warm) · 64 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
ChatCRuns well25.7 tok/s4108 ms40K
CodingCTight fit25.7 tok/s7532 ms40K
Agentic CodingCRuns with offload25.7 tok/s10955 ms40K
ReasoningCTight fit25.7 tok/s8901 ms40K
RAGCRuns with offload25.7 tok/s13694 ms40K

Quantization options

How speechless zephyr code functionary 7b (7B params) fits at each quantization level on RX 6600 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

Opciones de mejora

Hardware que ejecuta bien speechless zephyr code functionary 7b

RX 9060 XT 16GBOpción económica
16 GB VRAM (+8)320 GB/s (+96)
C
Sube la velocidad estimada de decodificación alrededor de un 84%.47.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 84%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$349 MSRP

RX 7700 XT 12GBMejor relación calidad-precio
12 GB VRAM (+4)432 GB/s (+208)
C
Sube la velocidad estimada de decodificación alrededor de un 136%.60.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 136%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$449 MSRP

RX 6700 XT 12GBMejora AMD
12 GB VRAM (+4)384 GB/s (+160)
C
Sube la velocidad estimada de decodificación alrededor de un 82%.46.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 82%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$479 MSRP

Frequently asked questions

Can RX 6600 8GB run speechless zephyr code functionary 7b?

Yes, RX 6600 8GB can run speechless zephyr code functionary 7b with a C grade (Tight fit). Expected decode speed: 25.7 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 6600 8GB?

On RX 6600 8GB, speechless zephyr code functionary 7b achieves approximately 25.7 tokens per second decode speed with a time-to-first-token of 7532ms using Q4_K_M quantization.

Can RX 6600 8GB run speechless zephyr code functionary 7b for coding?

For coding workloads, speechless zephyr code functionary 7b on RX 6600 8GB receives a C grade with 25.7 tok/s and 40K context.

What context window can speechless zephyr code functionary 7b use on RX 6600 8GB?

On RX 6600 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 6600 8GBSee all hardware for speechless zephyr code functionary 7b
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