Zephyr 7B Beta

7B

HuggingFaceH4

Superseded · Prefer Qwen3 8B Instruct

This index would suggest Qwen3 8B Instruct instead today — longer context (32K → 40K tokens). The numbers below are still accurate; they are just for a model you probably should not start with. Compare the two

DPO-aligned Mistral 7B. Classic choice for helpful, harmless chat baselines.

Consumer GPUMac / Apple SiliconCPU / VPS

33K

Max Context

2

Quant Variants

GGUF Q6_K

Best Quality

99.0%

Accuracy Retained

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = site benchmarks · Estimated = formula · Community = public reports

FormatLevelBPWVRAMPPL LossSpeedSourceActions
GGUFQ4_K_M4.855.2 GB3.3%155 tok/sEstimated
CalcHF
GGUFQ6_K6.566.8 GB1.0%132 tok/sEstimated
CalcHF

Running Zephyr 7B Beta locally

At Q4_K_M and 4K of context, Zephyr 7B Beta needs about 5.1 GB — 4.2 GB of weights, 0.50 GB of KV cache and a 0.5 GB activation buffer. The smallest card in this index that clears that comfortably is the RTX 5060 Ti 8G at 8 GB, and 63 of the 63 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 3.50 GB, taking the total to 9.0 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 32K; holding all of it at Q4_K_M would need about 9 GB.

Which build to download

This index only tracks Zephyr 7B Beta in GGUF, across 2 levels (Q4_K_M, Q6_K). Q6_K carries the lowest published perplexity loss at 1.0%. The fastest level measured here is Q4_K_M at 155 tok/s on an RTX 4090, batch 1. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 and CUDA only.

Common questions

How much VRAM does Zephyr 7B Beta need?
About 5.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 9.0 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
Will Zephyr 7B Beta run on a 8GB GPU?
Yes — at Q4_K_M and 4K context it needs about 5.1 GB, which leaves 2.9 GB spare on a RTX 5060 Ti 8G. That is the smallest card in this index that clears it comfortably; 63 of 63 do.
Which quantization of Zephyr 7B Beta should I use?
Q6_K has the lowest published quality loss (1.0%), and Q4_K_M is the level most people run. All 2 levels in the index are Q4_K_M, Q6_K.

Where this model fits

Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.

Ships in
GGUF

This page's figures change when the model or the runtime does.Last updated 2026-09-21 RSS → /feed.xml