Can All MiniLM L6 v2 run on RTX 5080 16GB?

Q: What should I upgrade first if All MiniLM L6 v2 feels slow on RTX 5080 16GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — Runs Great

B62Good

Estimated from fit model

All MiniLM L6 v2 needs ~3.1 GB VRAM. RTX 5080 16GB has 16.0 GB. With F16 quantization, expect ~2 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Memory bandwidth

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

F16 (Maximum quality) — 3.1 GB, 2.0 tok/s, Runs well

3.1 GB required16.0 GB available

19% VRAM used

Fit status

Runs well

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

256

Memory

3.1 GB / 16.0 GB

Memory breakdown

Weights0.0 GB

KV Cache0.3 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsAll MiniLM L6 v2 on RTX 5080 16GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

This model fits, but memory bandwidth is the part holding decode speed back.

Throughput will feel slow

Estimated decode speed is only 2.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

Best improvement path

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	2.0 tok/s	52800 ms	256
Coding	B	Runs well	2.0 tok/s	96800 ms	256
Agentic Coding	B	Runs well	2.0 tok/s	140800 ms	256
Reasoning	B	Runs well	2.0 tok/s	114400 ms	256
RAG	B	Runs well	2.0 tok/s	176000 ms	256

Inference speed

All MiniLM L6 v2 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for All MiniLM L6 v2 at F16 across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~2 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	F16	2.0	Fits
RTX 4090 24GB	24 GB	F16	2.0	Fits
RTX 4080 Super 16GB	16 GB	F16	2.0

Quantization options

How All MiniLM L6 v2 (0.023000000044703484B params) fits at each quantization level on RTX 5080 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.0 GB	Low	A72
Q3_K_S	3	0.0 GB	Low	A72
NVFP4	4

Get started

Copy-paste commands to run All MiniLM L6 v2 on your machine.

Run

ollama run all-minilm

Upgrade options

Hardware that runs All MiniLM L6 v2 well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+16)

Adds memory headroom for longer context windows and future model growth.2 tok/s decode

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

~$799 MSRP

MacBook Pro M3 24GBBest value

24 GB Unified (+8)

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.2 tok/s decode

~$1,099 MSRP

Frequently asked questions

Can RTX 5080 16GB run All MiniLM L6 v2?

Yes, RTX 5080 16GB can run All MiniLM L6 v2 with a B grade (Runs well). Expected decode speed: 2.0 tok/s.

How much VRAM does All MiniLM L6 v2 need?

All MiniLM L6 v2 (0.023000000044703484B parameters) requires approximately 3.1 GB of memory with F16 quantization.

What is the best quantization for All MiniLM L6 v2?

The recommended quantization for All MiniLM L6 v2 is F16, which balances quality and memory efficiency.

What speed will All MiniLM L6 v2 run at on RTX 5080 16GB?

On RTX 5080 16GB, All MiniLM L6 v2 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using F16 quantization.

Can RTX 5080 16GB run All MiniLM L6 v2 for coding?

For coding workloads, All MiniLM L6 v2 on RTX 5080 16GB receives a B grade with 2.0 tok/s and 256 context.

What context window can All MiniLM L6 v2 use on RTX 5080 16GB?

On RTX 5080 16GB, All MiniLM L6 v2 can safely use up to 256 tokens of context. The model's official context limit is 256, but available memory constrains the safe maximum.

What should I upgrade first if All MiniLM L6 v2 feels slow on RTX 5080 16GB?

See all results for RTX 5080 16GB See all hardware for All MiniLM L6 v2

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