DeepSeek-R1-Distill-Qwen-7B vs Llama-8B VRAM

Short answer: DeepSeek’s official full-precision repositories are close in weight size—15.2GB for R1-Distill-Qwen-7B and 16.1GB for R1-Distill-Llama-8B—but their cache geometry is not close. At the same token allocation, the Llama-8B configuration has more than twice the illustrative FP16 KV payload of the Qwen-7B configuration. Both can target compact 4-bit use on 8GB–12GB hardware, but context can change which fit is easier.

Official 7B vs 8B comparison

Published field	Distill-Qwen-7B	Distill-Llama-8B	Why it matters
Base model	Qwen2.5-Math-7B	Llama 3.1 8B	Different architecture and tokenizer families
Official repository display	15.2GB	16.1GB	Similar full-precision checkpoint class
Layers	28	32	Multiplies per-sequence cache state
Attention / KV heads	28 / 4	32 / 8	The Llama model stores twice as many KV heads per layer
Head dimension	128	128	Derived from published hidden size and attention heads
Maximum positions	131,072	131,072	A model limit, not a hardware-fit promise

Raw weight floors

Nominal precision	7B raw floor	8B raw floor	Planning implication
4-bit	3.5GB	4GB	8GB can be a compact-artifact starting tier
5-bit	4.375GB	5GB	More room on 12GB, especially as context grows
6-bit	5.25GB	6GB	Eight gigabytes becomes more workload-sensitive
8-bit	7GB	8GB	No useful margin on an 8GB label before overhead
16-bit	14GB	16GB	Consistent with the official 15.2GB and 16.1GB displays

KV-cache comparison at 8K, 32K and 128K

For one sequence with FP16 key/value entries, use 2 × layers × KV heads × head dimension × 2 bytes × token slots.

Allocated token slots	Qwen-7B illustrative FP16 K+V	Llama-8B illustrative FP16 K+V
8,192	0.4375GiB	1GiB
32,768	1.75GiB	4GiB
131,072	7GiB	16GiB

Can they run on 4GB?

The nominal 4-bit floors are already 3.5GB and 4GB before artifact overhead, cache and runtime state. Very low-bit conversions or CPU offload can make some configurations load, but 4GB is a constrained experiment rather than a reliable all-GPU tier for either model.

Is 8GB enough?

Eight gigabytes can target a compact 4-bit conversion at modest context. The Qwen-7B model’s smaller cache geometry gives it more context margin at the same cache precision and token allocation. That does not prove higher answer quality or faster generation; it only describes one memory component.

What does 12GB change?

Twelve gigabytes creates room for larger quantizations, longer context or runtime overhead. It is still not a universal 128K tier: the illustrative FP16 cache alone is about 7GiB for Qwen-7B and 16GiB for Llama-8B at 131,072 slots, before weights. Quantized cache, shorter context or offload can alter the result.

Which one should you choose?

• Choose Qwen-7B when its tokenizer/runtime ecosystem and smaller cache geometry match the workload.
• Choose Llama-8B when Llama compatibility or its output behavior is the priority and the extra cache cost is acceptable.
• Choose neither by VRAM alone: test the exact quantization on representative prompts with identical output limits and generation settings.

A reproducible comparison checklist

1. Record both complete repository names, revisions and quantizations.
2. Use each model’s intended tokenizer and prompt template.
3. Hold context, cache precision, batch, concurrency and maximum output constant.
4. Measure peak device and host memory separately.
5. Evaluate answer quality with the same prompt set.
6. Record prompt processing, generation speed and time to first token separately.
7. Do not infer a full 128K fit from a short successful request.

Sources and limits

• Official Distill-Qwen-7B repository
• Official Qwen-7B configuration
• Official Distill-Llama-8B repository
• Official Llama-8B configuration
• Official DeepSeek-R1 model table
• HighVRAM methodology

Reviewed October 25, 2026. Repository displays and architecture fields come from DeepSeek’s official pages. Weight and cache calculations are transparent planning arithmetic, not measured peak-memory claims. HighVRAM has not independently benchmarked every configuration. Capacity does not establish speed, reasoning quality or value.