Can Qwen3.5 27B run on RTX A4500 20GB?

BARELY — Tight on Memory

D39Poor

Estimated from fit model

Qwen3.5 27B needs ~22.5 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 22.5 GB, 17.7 tok/s, Very compromised (needs ~1.9 GB host RAM)

22.5 GB required20.0 GB available

113% VRAM needed

2.5 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.9 GB host RAM)

Decode

17.7 tok/s

TTFT

10948 ms

Safe context

Memory

22.5 GB / 20.0 GB

Offload

10%

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsQwen3.5 27B on RTX A4500 20GB
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: 17.7 tok/s decode · 10.9s TTFT (warm) · 44 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0.7 GB host RAM)	20.6 tok/s	5123 ms	4K
Coding	D	Very compromised (needs ~1.9 GB host RAM)	17.7 tok/s	10948 ms	4K
Agentic Coding	F	Too heavy	13.4 tok/s	20997 ms	4K
Reasoning	D	Very compromised (needs ~1.9 GB host RAM)	17.7 tok/s	12938 ms	4K
RAG	F	Too heavy	13.4 tok/s	26247 ms

Quantization options

How Qwen3.5 27B (27B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	C51
Q3_K_S	3	13.2 GB	Low	C51
NVFP4Best for your GPU

Get started

Copy-paste commands to run Qwen3.5 27B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "unsloth/Qwen3.5-27B-GGUF" \
  --hf-file "Qwen3.5-27B-GGUF-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Qwen3.5 27B well

RTX 3090 24GBBudget pick

24 GB VRAM (+4)936 GB/s (+296)

Removes host-memory offload, which is usually the single biggest latency and throughput win.27.1 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 53%.

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBBest value

24 GB VRAM (+4)672 GB/s (+32)

Removes host-memory offload, which is usually the single biggest latency and throughput win.34.3 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 94%.

~$1,599 MSRP

RTX A5500 24GBNVIDIA upgrade

24 GB VRAM (+4)768 GB/s (+128)

Removes host-memory offload, which is usually the single biggest latency and throughput win.36.4 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 106%.

~$3,200 MSRP

Frequently asked questions

Can RTX A4500 20GB run Qwen3.5 27B?

Yes, RTX A4500 20GB can run Qwen3.5 27B with a D grade (Very compromised (needs ~1.9 GB host RAM)). Expected decode speed: 17.7 tok/s.

How much VRAM does Qwen3.5 27B need?

Qwen3.5 27B (27B parameters) requires approximately 22.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 27B?

The recommended quantization for Qwen3.5 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 27B run at on RTX A4500 20GB?

On RTX A4500 20GB, Qwen3.5 27B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10948ms using Q4_K_M quantization.

Can RTX A4500 20GB run Qwen3.5 27B for coding?

For coding workloads, Qwen3.5 27B on RTX A4500 20GB receives a D grade with 17.7 tok/s and 4K context.

What context window can Qwen3.5 27B use on RTX A4500 20GB?

On RTX A4500 20GB, Qwen3.5 27B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3.5 27B feels slow on RTX A4500 20GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX A4500 20GB See all hardware for Qwen3.5 27B

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