Can Dolphin 2.9 8B run on NVIDIA A10 24GB?

YES — Runs Great

C52Usable

Estimated from fit model

Dolphin 2.9 8B needs ~10.4 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q4_K_M quantization, expect ~103 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 10.4 GB, 103.1 tok/s, Runs well

10.4 GB required24.0 GB available

43% VRAM used

Fit status

Runs well

Decode

103.1 tok/s

TTFT

1878 ms

Safe context

33K

Memory

10.4 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsDolphin 2.9 8B on NVIDIA A10 24GB
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: 103.1 tok/s decode · 1.9s TTFT (warm) · 258 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	103.1 tok/s	1024 ms	33K
Coding	C	Runs well	103.1 tok/s	1878 ms	33K
Agentic Coding	C	Runs well	103.1 tok/s	2731 ms	33K
Reasoning	C	Runs well	103.1 tok/s	2219 ms	33K
RAG	C	Runs well	103.1 tok/s	3414 ms	33K

Inference speed

Dolphin 2.9 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Dolphin 2.9 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 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	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	102.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	96.9	Fits
RTX 4070 12GB	12 GB	Q4_K_M	83.3	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	82.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	82.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	52.9	Fits
RTX 3060 12GB	12 GB	Q4_K_M	52.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	48.5	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	42.6	Fits
RTX 4060 8GB	8 GB	Q4_K_M	26.6	Heavy offload

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 Dolphin 2.9 8B (8B params) fits at each quantization level on NVIDIA A10 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C45
Q3_K_S	3	3.9 GB	Low	C46
NVFP4	4	4.5 GB	Medium	C46
Q4_K_M	4	4.9 GB	Medium	C46
Q5_K_M	5	5.8 GB	High	C47
Q6_K	6	6.6 GB	High	C47
Q8_0	8	8.6 GB	Very High	C48
F16Best for your GPU	16	16.4 GB	Maximum	C50

Get started

Copy-paste commands to run Dolphin 2.9 8B on your machine.

Run

ollama run dolphin-llama3

Frequently asked questions

Can NVIDIA A10 24GB run Dolphin 2.9 8B?

Yes, NVIDIA A10 24GB can run Dolphin 2.9 8B with a C grade (Runs well). Expected decode speed: 103.1 tok/s.

How much VRAM does Dolphin 2.9 8B need?

Dolphin 2.9 8B (8B parameters) requires approximately 10.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Dolphin 2.9 8B?

The recommended quantization for Dolphin 2.9 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Dolphin 2.9 8B run at on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Dolphin 2.9 8B achieves approximately 103.1 tokens per second decode speed with a time-to-first-token of 1878ms using Q4_K_M quantization.

Can NVIDIA A10 24GB run Dolphin 2.9 8B for coding?

For coding workloads, Dolphin 2.9 8B on NVIDIA A10 24GB receives a C grade with 103.1 tok/s and 33K context.

What context window can Dolphin 2.9 8B use on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Dolphin 2.9 8B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for NVIDIA A10 24GB See all hardware for Dolphin 2.9 8B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

103.1 tok/s

1024 ms

33K

Coding

Runs well

103.1 tok/s

1878 ms

33K

Agentic Coding

Runs well

103.1 tok/s

2731 ms

33K

Reasoning

Runs well

103.1 tok/s

2219 ms

33K

RAG

Runs well

103.1 tok/s

3414 ms

33K

Dolphin 2.9 8B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

112.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

112.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

112.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

112.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

112.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

112.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

102.2

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

96.9

Fits

RTX 4070 12GB

12 GB

Q4_K_M

83.3

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

82.6

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

82.6

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

52.9

Fits

RTX 3060 12GB

12 GB

Q4_K_M

52.3

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

48.5

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

42.6

Fits

RTX 4060 8GB

8 GB

Q4_K_M

26.6

Heavy offload

Quantization options

How Dolphin 2.9 8B (8B params) fits at each quantization level on NVIDIA A10 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C45
Q3_K_S	3	3.9 GB	Low	C46
NVFP4	4	4.5 GB	Medium	C46
Q4_K_M	4	4.9 GB	Medium	C46
Q5_K_M	5	5.8 GB	High	C47
Q6_K	6	6.6 GB	High	C47
Q8_0	8	8.6 GB	Very High	C48
F16Best for your GPU	16	16.4 GB	Maximum	C50

Frequently asked questions

Can NVIDIA A10 24GB run Dolphin 2.9 8B?

Yes, NVIDIA A10 24GB can run Dolphin 2.9 8B with a C grade (Runs well). Expected decode speed: 103.1 tok/s.

How much VRAM does Dolphin 2.9 8B need?

Dolphin 2.9 8B (8B parameters) requires approximately 10.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Dolphin 2.9 8B?

The recommended quantization for Dolphin 2.9 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Dolphin 2.9 8B run at on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Dolphin 2.9 8B achieves approximately 103.1 tokens per second decode speed with a time-to-first-token of 1878ms using Q4_K_M quantization.

Can NVIDIA A10 24GB run Dolphin 2.9 8B for coding?

For coding workloads, Dolphin 2.9 8B on NVIDIA A10 24GB receives a C grade with 103.1 tok/s and 33K context.

What context window can Dolphin 2.9 8B use on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Dolphin 2.9 8B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.