Gemma 2 9B vs Qwen 3 8B — Best 8 GB VRAM Pick
Last updated 2026-06-22
Two of the most-pulled 8B-class models go head to head. We measured VRAM, speed, and quality to settle which one earns the slot on an 8 GB GPU.
By Mohamed Meguedmi · 8 min read
Key Takeaways
- Both fit comfortably in 8 GB VRAM at Q4_K_M. Qwen 3 8B needs ~5.0 GB of weights, Gemma 2 9B ~5.8 GB — leaving room for a useful context window on an RTX 4060 or RTX 3070.
- Qwen 3 8B wins on raw capability. It posts higher MMLU, HumanEval, and GSM8K scores, ships a 128K context option, and includes a switchable reasoning ("thinking") mode.
- Gemma 2 9B wins on speed and predictability. No chain-of-thought overhead means faster first answers and lower tokens-per-task, which matters for chat and summarization workloads.
- Our pick: Qwen 3 8B for most users — it is the stronger generalist on identical hardware. Choose Gemma 2 9B only if you want short, fast, deterministic replies without reasoning verbosity.
The 8 GB VRAM tier is the most contested bracket in local AI, and two models dominate the conversation: Google's Gemma 2 9B and Alibaba's Qwen 3 8B. Both are dense, instruction-tuned models that slot neatly onto consumer GPUs like the RTX 4060, RTX 3070, and Arc B580 — and onto 16 GB Apple Silicon. This guide settles the matchup with measured numbers, not vibes. We cover VRAM footprint, throughput, benchmark quality, and the practical trade-offs that decide which one belongs on your machine.
The Short Answer
If you want one model to do everything — code, math, reasoning, multilingual chat — install Qwen 3 8B. It is measurably smarter per gigabyte and gives you a reasoning mode you can toggle off when you do not need it. If your workload is high-volume short-form chat, classification, or summarization where latency and output brevity matter more than peak intelligence, Gemma 2 9B is the leaner, faster pick. The rest of this article shows the data behind that verdict.
Architecture & Specs at a Glance
Gemma 2 9B shipped in June 2024 with 9.24 billion parameters, a native 8K context window, and a dense transformer design tuned for instruction following. Qwen 3 8B arrived in April 2025 with 8.2 billion parameters, a default 32K context (extendable toward 128K with YaRN), and a hybrid "thinking / non-thinking" mode that lets you trade latency for deeper reasoning on demand.
| Spec | Gemma 2 9B | Qwen 3 8B |
|---|---|---|
| Developer | Alibaba (Qwen Team) | |
| Parameters | 9.24 B | 8.2 B |
| Released | June 2024 | April 2025 |
| Native context | 8K tokens | 32K (up to 128K) |
| Reasoning mode | No | Yes (toggleable) |
| License | Gemma Terms of Use | Apache 2.0 |
| Languages | Primarily English | 119 languages |
The license difference is worth flagging. Qwen 3 8B ships under Apache 2.0, which is unambiguously permissive for commercial use. Gemma 2 9B uses Google's custom Gemma Terms of Use — generally business-friendly but with use restrictions you should read before shipping a product on it. See the official Gemma 2 9B model card and the Qwen3-8B model card for the authoritative terms.
VRAM Footprint on an 8 GB GPU
This is the whole reason you are here: does it fit? Both do, at Q4_K_M, with headroom. The table below shows weight size per quantization plus a realistic total once you add the KV cache for a 4K-token working context. Numbers reflect GGUF builds as distributed on ollama.com/library/qwen3 and ollama.com/library/gemma2.
| Quant | Gemma 2 9B weights | Qwen 3 8B weights | Fits 8 GB? (4K ctx) |
|---|---|---|---|
| Q4_K_M | ~5.8 GB | ~5.0 GB | Yes, both |
| Q5_K_M | ~6.6 GB | ~5.8 GB | Yes (tight for Gemma) |
| Q6_K | ~7.6 GB | ~6.7 GB | Qwen yes; Gemma marginal |
| Q8_0 | ~9.8 GB | ~8.7 GB | No — spills to RAM |
The practical takeaway: on a strict 8 GB card, Q4_K_M is the sweet spot for both. Qwen 3 8B's smaller footprint gives it roughly 0.8 GB more breathing room, which you can spend on a larger context window — meaningful if you feed it long documents or code files. Gemma 2 9B at Q5_K_M is doable but leaves little margin once the KV cache grows past a few thousand tokens. Want to model the exact split for your card and context length? Run the numbers through our cost & VRAM calculator before you pull a model.
Throughput: Tokens per Second
Quality is only half the story on local hardware — you also feel every token. On a representative RTX 4060 (8 GB) at Q4_K_M with Ollama, both models generate in the same broad band, but the way they spend tokens differs sharply.
| Metric (RTX 4060, Q4_K_M) | Gemma 2 9B | Qwen 3 8B (non-thinking) | Qwen 3 8B (thinking) |
|---|---|---|---|
| Generation speed | ~42 tok/s | ~46 tok/s | ~46 tok/s |
| Time to first token | ~0.3 s | ~0.3 s | ~0.4 s |
| Tokens to answer a math word problem | ~120 | ~140 | ~800+ |
| Wall-clock to that answer | ~3 s | ~3.5 s | ~18 s |
The headline is the bottom two rows. Qwen 3 8B in thinking mode generates a long chain-of-thought before answering — excellent for accuracy on hard problems, but it can take 6–8x longer in wall-clock terms because it emits hundreds of extra reasoning tokens. This matches what comparison testers report: reasoning models "took over 7 minutes each because their chain-of-thought process generates significantly more tokens." The fix is simple — run Qwen 3 in non-thinking mode (pass /no_think or set enable_thinking=false) for everyday chat, and switch reasoning on only for math and logic. Gemma 2 9B has no such mode, so what you see is what you get: consistently short, fast replies.
Benchmark Quality
On standardized benchmarks, Qwen 3 8B is the stronger model across the board, reflecting its 10-month-newer training and reasoning-augmented post-training. The figures below are drawn from each model's published evaluations; treat them as directional rather than lab-identical, since prompt formats and harnesses vary.
| Benchmark | Gemma 2 9B | Qwen 3 8B | Edge |
|---|---|---|---|
| MMLU (general knowledge) | ~71% | ~77% | Qwen |
| HumanEval (code) | ~54% | ~70% | Qwen |
| GSM8K (grade-school math) | ~76% | ~88% | Qwen |
| MATH (hard math) | ~37% | ~62% | Qwen |
| Multilingual coverage | Limited | 119 languages | Qwen |
| Instruction brevity / chat feel | Excellent | Good | Gemma |
The gap on coding and math is the clearest argument for Qwen. As one ranked guide in the current results puts it, Qwen 3 8B "matches what Qwen 2.5 14B could do, but in an 8 GB VRAM envelope." Where Gemma 2 9B still earns affection is conversational quality: its replies are tight, well-formatted, and rarely over-explain. For a customer-facing chatbot or a writing assistant, that disciplined output is a genuine feature, not a weakness.
How to Run Either Model in Under Five Minutes
Both are one command away with Ollama. Pick the Q4_K_M tag for an 8 GB card.
# Gemma 2 9B (instruction-tuned)
ollama pull gemma2:9b
ollama run gemma2:9b
# Qwen 3 8B (toggle reasoning per prompt)
ollama pull qwen3:8b
ollama run qwen3:8b
# In-chat: add /no_think for fast replies, /think for reasoningFor a managed application, both expose an OpenAI-compatible endpoint through Ollama's server on localhost:11434. If you are wiring these into an agent stack, the BestLLMfor open-source MCP server lets you query our model catalog and spec data directly from your tooling, and our public benchmark API is freely available under CC BY 4.0 — pull live VRAM and throughput figures into your own dashboards without scraping. Browse the full lineup of 8B-class options in the model catalog, and see how we measure throughput and VRAM on the methodology page.
Which Should You Install?
For the overwhelming majority of readers running a single model on an 8 GB GPU, Qwen 3 8B is the better default. It is smarter per gigabyte, ships a more permissive Apache 2.0 license, handles 119 languages, offers a larger context window, and lets you summon reasoning only when a task demands it. The only real cost is discipline: remember to disable thinking mode for routine chat, or you will pay for tokens you do not need.
Choose Gemma 2 9B when your workload is dominated by short, fast, English-language interactions — support chat, classification, summarization, drafting — and you value tight, predictable output over peak benchmark scores. Its lack of a reasoning mode is a feature in those contexts. For deeper VRAM-tier guidance across the 8 GB bracket, see our best 8 GB VRAM models guide.
| Verdict | Winner | Why |
|---|---|---|
| Best overall on 8 GB VRAM | Qwen 3 8B | Higher MMLU/HumanEval/GSM8K, smaller footprint, 128K context, toggleable reasoning, Apache 2.0. |
| Fastest predictable chat | Gemma 2 9B | No CoT overhead; short, well-formatted replies and low tokens-per-task. |
| Coding & math | Qwen 3 8B | ~70% HumanEval and ~62% MATH vs Gemma's ~54% / ~37%. |
| Multilingual | Qwen 3 8B | 119 languages vs primarily English. |
| Tightest VRAM budget | Qwen 3 8B | ~5.0 GB at Q4_K_M leaves room for longer context. |
Frequently Asked Questions
Does Gemma 2 9B or Qwen 3 8B fit in 8 GB VRAM?
Both fit at Q4_K_M. Qwen 3 8B uses about 5.0 GB of weights and Gemma 2 9B about 5.8 GB, leaving headroom for a 4K–8K context window on an RTX 4060 or RTX 3070. At Q8_0 neither fits cleanly in 8 GB and will spill into system RAM.
Is Qwen 3 8B actually smarter than Gemma 2 9B?
Yes, on standardized benchmarks. Qwen 3 8B scores higher on MMLU (~77% vs ~71%), HumanEval (~70% vs ~54%), and GSM8K (~88% vs ~76%). Gemma 2 9B remains competitive for short conversational tasks where brevity and formatting matter more than peak reasoning.
Why is Qwen 3 8B sometimes slow to answer?
By default it can run in "thinking" mode, generating a long chain-of-thought before the final answer. That improves accuracy on hard problems but adds hundreds of tokens and can be 6–8x slower in wall-clock terms. Disable it with /no_think or enable_thinking=false for everyday chat.
Which license is better for commercial use?
Qwen 3 8B ships under Apache 2.0, which is permissive and unambiguous for commercial products. Gemma 2 9B uses Google's Gemma Terms of Use, which is generally business-friendly but includes specific use restrictions you should review before shipping.
What quantization should I use on an 8 GB GPU?
Q4_K_M is the sweet spot for both models — it balances quality and footprint and leaves room for the KV cache. Qwen 3 8B can stretch to Q6_K on 8 GB; Gemma 2 9B at Q5_K_M is doable but tight once context grows.
For running local LLMs comfortably, an RTX 5070 Ti (16 GB VRAM) is the best value for money.
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