Large VRAM and Long Context Limits

Large VRAM and Long Context Limits | Generated by AI

Home 2026.07

Question: Does a 48GB VRAM setup (2×RTX 3090) still fit large context windows like 32k or 100k tokens?

Answer:

The short answer: model weights fit, but long context is where VRAM gets eaten by KV cache. For 32k it is usually fine; 100k depends heavily on model architecture and quantization.

The inference memory is:

Total VRAM =
  model weights
+ KV cache
+ CUDA/runtime overhead

Most people only calculate weights and forget KV cache.

Example: Qwen2.5-Coder-32B / Qwen3-32B

Assume:

32B model
4-bit weights
48GB VRAM

Weights:

32B × 4 bits
= 16GB

Runtime:

~3-5GB

Leaves:

48 - 20 ≈ 28GB

for KV cache.

KV cache calculation

For a transformer:

KV cache =
2 × layers × sequence_length × KV_heads × head_dim × bytes

The important variables:

number of layers
KV heads (GQA helps a lot)
context length
KV cache precision

Modern models use GQA:

Example Qwen2.5-32B:

layers = 64
KV heads = 8
head_dim = 128

FP16 KV:

2 × 64 × 32768 × 8 × 128 × 2 bytes

≈ 8.6 GB

So:

32k context

weights      ~16GB
runtime      ~4GB
KV cache     ~9GB

total        ~29GB

Easy on 48GB.

100k context

Scale linearly:

100k / 32k = 3.125

KV:

9GB × 3.125
≈ 28GB

Total:

weights      16GB
runtime       4GB
KV           28GB

≈48GB

So Qwen2.5-32B 4-bit at 100k is right at the edge.

You would probably need:

KV cache quantization
lower batch size
llama.cpp
vLLM paged attention

For 70B models

Example:

Llama 3.1 70B Q4:

Weights:

~40GB

Runtime:

5GB

Remaining:

3GB

KV cache:

not much room.

So:

70B @ 32k

is difficult on 48GB.

You need:

KV cache quantization
CPU offload
more GPUs

KV cache quantization changes everything

Instead of FP16 KV:

K,V = 16-bit

use:

INT8 KV

or:

FP8 KV

Memory roughly halves.

Example:

100k context:

FP16 KV:
28GB

FP8 KV:
14GB

Then:

32B Q4:
16GB weights
5GB runtime
14GB KV

≈35GB

Very comfortable.

Practical 2×3090 targets

Model	32k ctx	100k ctx
Qwen3 8B	✅ easy	✅ easy
Qwen3 14B	✅ easy	✅
Qwen3 32B Q4	✅	⚠️ with KV quant
Qwen2.5-Coder 32B Q4	✅	⚠️
Llama 3.1 70B Q4	⚠️	❌
DeepSeek-R1-Distill-32B	✅	⚠️

For your workload (agents + coding + long documents), I would actually prioritize:

2 × RTX3090 48GB
+
llama.cpp / vLLM
+
Qwen3-32B
+
FP8/INT8 KV cache

This gives you a local model with something close to 100k context capability, which is much more useful than squeezing a larger 70B model with only 8k context.

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