Will It Run AI

Can HelpingAI2.5 10B i1 run on RX 6700 XT 12GB?

YES — Runs Great

C53Usable
Estimated from fit model

HelpingAI2.5 10B i1 needs ~9.4 GB VRAM. RX 6700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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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) — 9.4 GB, 32.7 tok/s, Runs well
9.4 GB required12.0 GB available
78% VRAM used

Fit status

Runs well

Decode

32.7 tok/s

TTFT

5914 ms

Safe context

52K

Memory

9.4 GB / 12.0 GB

Memory breakdown

Weights6.1 GB
KV Cache1.2 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 10B i1 on RX 6700 XT 12GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 32.7 tok/s decode · 5.9s TTFT (warm) · 82 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 well32.7 tok/s3226 ms52K
CodingCRuns well32.7 tok/s5914 ms52K
Agentic CodingCTight fit32.7 tok/s8603 ms52K
ReasoningCRuns well32.7 tok/s6990 ms52K
RAGCTight fit32.7 tok/s10753 ms52K

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on RX 6700 XT 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.9 GB
LowC50
Q3_K_S
3
4.9 GB
LowC51
NVFP4
4
5.6 GB
MediumC52
Q4_K_M
4
6.1 GB
MediumC52
Q5_K_M
5
7.2 GB
HighC51
Q6_KBest for your GPU
6
8.2 GB
HighC51
Q8_0
8
10.7 GB
Very HighF0
F16
16
20.5 GB
MaximumF0

Get started

Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.

Run

lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien HelpingAI2.5 10B i1

RX 9070 16GBOpción económica
16 GB VRAM (+4)640 GB/s (+256)
C
Sube la velocidad estimada de decodificación alrededor de un 99%.65 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 99%.

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

~$479 MSRP

RX 7800 XT 16GBMejor relación calidad-precio
16 GB VRAM (+4)624 GB/s (+240)
C
Sube la velocidad estimada de decodificación alrededor de un 94%.63.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 94%.

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

~$499 MSRP

RX 9070 XT 16GBMejora AMD
16 GB VRAM (+4)640 GB/s (+256)
C
Sube la velocidad estimada de decodificación alrededor de un 105%.67.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 105%.

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

~$549 MSRP

Frequently asked questions

Can RX 6700 XT 12GB run HelpingAI2.5 10B i1?

Yes, RX 6700 XT 12GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 32.7 tok/s.

How much VRAM does HelpingAI2.5 10B i1 need?

HelpingAI2.5 10B i1 (10B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2.5 10B i1?

The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2.5 10B i1 run at on RX 6700 XT 12GB?

On RX 6700 XT 12GB, HelpingAI2.5 10B i1 achieves approximately 32.7 tokens per second decode speed with a time-to-first-token of 5914ms using Q4_K_M quantization.

Can RX 6700 XT 12GB run HelpingAI2.5 10B i1 for coding?

For coding workloads, HelpingAI2.5 10B i1 on RX 6700 XT 12GB receives a C grade with 32.7 tok/s and 52K context.

What context window can HelpingAI2.5 10B i1 use on RX 6700 XT 12GB?

On RX 6700 XT 12GB, HelpingAI2.5 10B i1 can safely use up to 52K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 6700 XT 12GBSee all hardware for HelpingAI2.5 10B i1
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