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Start with Qwen 3.5 4B. NVIDIA currently places it in the starting tier for RTX graphics cards with 6–8GB of VRAM, making it the clearest first model to try on an 8GB card. That is vendor guidance, not a head-to-head benchmark.
Qwen 3.5 9B is listed by NVIDIA in a broader 8–16GB llama.cpp tier, so it may be testable on an 8GB card with an appropriate quantized build and conservative settings. It is not a guarantee that every 9B model, format or context length will fit. Your usable limit depends on the model file, quantization, context window, GPU, driver, operating system and inference backend.
The short list
| Model or guidance | What the published guidance says | How to treat it on an 8GB card |
|---|---|---|
| Qwen 3.5 4B | NVIDIA’s starting recommendation for RTX GPUs in the 6–8GB tier | Best first trial when you want the highest probability of a comfortable fit |
| Qwen 3.5 9B | NVIDIA lists it in an 8–16GB llama.cpp tier | Worth testing only with a suitable quantized build, reduced context and room for runtime overhead; not a guaranteed full-GPU fit |
| Llama 3.1 8B in NVIDIA NIM | bf16: 16GB minimum and 33GB recommended; fp8: 8GB minimum and 16GB recommended | These figures apply to that NIM deployment, not to every GGUF, desktop runner or other quantized build |
No dependable, dated comparison of tokens per second for these models on a named 8GB card is established in the available guidance. Choose by task and test representative prompts instead of treating a model name or parameter count as a speed ranking.
Why Qwen 3.5 4B is the safest starting point
NVIDIA’s current RTX model guide puts Qwen 3.5 4B in the 6–8GB starting tier. For an 8GB card, that directly matches the capacity in your question. A 4B model generally leaves more room for the inference runtime, graphics-driver allocations and context cache than a larger model using the same card.
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“Starting point” does not mean universally best. A smaller model can be less capable on a difficult coding problem, a long document or a specialized question. It means the model is the least speculative recommendation in the published tiering. Download the exact quantized variant supported by your inference application, set a modest context first, and verify that the card remains stable while generating.
When it is a good choice
- Interactive chat where prompt and response quality matter more than maximum model size.
- Light coding assistance, explanations and transformations.
- Document question-answering with a moderate context window.
- Systems where other applications must continue using the GPU while the model runs.
What the recommendation does not prove
- It is not a controlled comparison against every other 4B or 7B model.
- It does not promise a particular token-per-second rate.
- It does not mean every Qwen 3.5 4B file has the same VRAM requirement; precision, context and backend still matter.
Should you try Qwen 3.5 9B?
NVIDIA’s separate llama.cpp playbook places Qwen 3.5 9B in an 8–16GB tier. That wider tier includes an 8GB boundary, but it should be read as a configuration to investigate, not as proof that a 9B model will fit entirely in your card’s VRAM.
A 9B attempt is most reasonable when you can use a memory-saving quantization, keep the context window under control and accept that some layers or buffers may need system RAM. Offloading can prevent an immediate out-of-memory error, but it can also reduce responsiveness. If your application cannot split work between GPU and CPU, the same model may fail where another backend succeeds.
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A practical 9B test
- Record the exact model variant and quantization, not just “Qwen 3.5 9B.”
- Close GPU-heavy applications and note the card’s idle and available VRAM.
- Start with the backend’s smallest sensible context setting.
- Load the model without a long document and generate a short response.
- Increase context or output length gradually while watching VRAM use and error logs.
- Keep the model only if it remains stable with the workload you actually intend to run.
If the 9B build needs aggressive quantization or constant CPU offload, Qwen 3.5 4B may provide a better day-to-day experience even if the larger model technically starts.
Why parameter count alone cannot answer “will it fit?”
Parameter count describes the scale of a model, but memory use is determined by more than that number. The stored weights, temporary working memory, key-value cache for the conversation and allocations made by the runtime all compete for the same hardware budget.
Precision and quantization
Higher-precision weights preserve more numerical information but consume more memory. Quantization stores weights at lower precision so a model can occupy less VRAM. NVIDIA describes quantization as a way to save memory while cautioning that aggressive settings can reduce answer quality.
Q4_K_M and NVFP4 are cited by NVIDIA as useful balance points in their respective guidance. They are not interchangeable labels: support and quality depend on the model format and backend. Select a build your runner explicitly supports and compare its answers on your own prompts. Do not assume that a file called “4-bit” has the same memory use or quality as every other 4-bit file.
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Context length is a second memory budget
The context window holds the system instructions, conversation history, retrieved documents and the tokens being generated. A longer context requires more memory, particularly for the key-value cache. A model that loads at a short context can fail after you paste a large document or enable a high maximum context.
Set context for the job rather than choosing the largest advertised number. For chat, begin with a moderate window. For document work, measure the size of the documents you actually pass and increase the setting only until it covers that use case.
Leave headroom
The model weights are not the entire VRAM budget. The driver, runtime, CUDA or ROCm buffers and other applications need space. “Fits” should mean that the model can load and complete your normal prompts without memory pressure, not merely that a loader reaches 100 percent once.
How to budget an 8GB card in practice
Think of 8GB as a ceiling shared by several allocations, not as 8GB reserved exclusively for model weights. Begin with the amount shown as free by your operating system or GPU monitor, then account for the runner’s runtime buffers, the model’s key-value cache and any graphical application still using the card. A model that leaves only a few hundred megabytes free may load successfully but fail when you submit a longer prompt.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchRun a baseline conversation before testing document retrieval or tool use. Record the context setting, maximum output and whether GPU layers are fully offloaded. When you change one variable at a time, you can tell whether a failure came from the quantized file, context growth or another application reclaiming VRAM. This also makes a setup reproducible if you later move from a desktop interface to llama.cpp or Ollama.
Do not compare download-file size directly with VRAM use. Files can be compressed on disk, while loading may require temporary buffers and format-specific workspace. Conversely, CPU offload can let a larger file start without fitting entirely in VRAM, but the extra transfers may make interaction feel much slower.
What the Llama 3.1 8B memory figures really mean
NVIDIA’s NIM system card illustrates why an “8B” label is not a universal hardware requirement. For Llama 3.1 8B in that NIM deployment, bf16 is listed at 16GB minimum and 33GB recommended. The fp8 entry is listed at 8GB minimum and 16GB recommended.
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The fp8 minimum is therefore an 8GB-capacity data point, but it is not a promise that an 8GB desktop card will run every Llama 3.1 8B implementation. NIM has its own container, runtime and serving assumptions. A GGUF file in a desktop runner, a different quantization or a different context setting can have a different footprint. Treat the figures as deployment-specific requirements, not as a conversion chart for all local LLM software.
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1. Identify the exact GPU
Record the model name, VRAM capacity and compute capability. Two cards with 8GB can have different driver support, memory bandwidth and acceleration features. Capacity alone does not establish compatibility.
2. Check the driver and operating system
Ollama’s GPU documentation lists NVIDIA support for compute capability 5.0 or newer with driver 550 or newer. It separately states that cards with compute capability 5.0 through 6.2 need driver 570 or newer. Those requirements are version-sensitive, so check the current compatibility page for your exact card rather than relying on a generic “NVIDIA supported” label.
Ollama also documents supported AMD GPU families through ROCm, with platform and driver conditions. AMD support is therefore a combination of GPU family, operating system and ROCm version; do not transfer NVIDIA assumptions to an AMD card.
3. Confirm the inference backend
Decide whether you will use a desktop application, llama.cpp, Ollama, NIM or another runner. Each backend supports particular model containers, quantization formats, GPU offload modes and context implementations. A model that works in one may require a different file or setting in another.
4. Choose the exact quantized file
Write down the format and quantization level before comparing memory use. “Qwen 3.5 4B” or “Llama 3.1 8B” is not enough information to reproduce a setup.
5. Set a realistic context
Use a context setting that covers your normal prompts, then leave memory for generation and the runtime. A large advertised maximum is optional capacity, not a requirement for every session.
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A repeatable way to choose among candidates
- Define the workload. Separate chat, coding and document Q&A. They stress different abilities and prompt lengths.
- Select two or three feasible builds. Begin with Qwen 3.5 4B, then add a larger or differently quantized candidate only if your hardware and backend support it.
- Create a small test set. Use prompts you can judge: a coding task with expected behavior, a factual question whose answer you know, and a document question with a clearly located answer.
- Keep conditions consistent. Use the same system prompt, context setting, temperature and output limit when comparing models.
- Score usefulness, not just fluency. Check correctness, instruction following, citation or quotation accuracy for documents, and whether code actually meets the requirement.
- Measure stability. Repeat a long prompt and a normal conversation. Watch for out-of-memory errors, unexplained truncation or severe CPU fallback.
- Choose the smallest model that meets the task. A model that leaves headroom can be more useful than a larger one that only works with restrictive settings.
NVIDIA’s local-AI guidance emphasizes selecting for the use case and evaluating candidate outputs. This process is more defensible than declaring one universal “best” model.
Use-case guidance
Chat and everyday questions
Start with Qwen 3.5 4B at a moderate context. Its capacity tier gives you the most room to keep other applications open and to increase the context later. If answers are not accurate enough, test a larger candidate only after confirming that the slower or more memory-intensive configuration is acceptable.
Coding
Test with the language, framework and repository style you actually use. Ask for a small change, an explanation of an existing function and a test. Record whether the model preserves interfaces and handles edge cases. A larger parameter count is not a substitute for a passing result.
Document question-answering
Context planning is as important as model choice. Estimate the tokens needed for instructions, retrieved passages, conversation history and the answer. If a long context causes memory failures, reduce retrieval size or use a shorter context before abandoning a model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common failure modes and fixes
The model fails during loading
- Confirm that the file is actually quantized and that the backend supports its format.
- Close other GPU applications and check available VRAM.
- Lower the context setting and reduce GPU offload if the backend permits CPU layers.
- Try Qwen 3.5 4B instead of forcing a 9B build into an 8GB budget.
It loads, then crashes on a long prompt
The additional context cache may have exhausted the remaining VRAM. Reduce maximum context, shorten retrieved text or leave more headroom by using a smaller quantization.
Responses are noticeably weaker after quantization
More aggressive quantization can trade quality for memory. Compare the same test prompts with a less aggressive file if it fits, and judge the result on your task rather than on a generic claim about bit depth.
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Check whether the backend placed some layers or buffers in system RAM. Verify that the intended GPU is selected and that the driver and runtime meet the backend’s requirements. Without a controlled test on your exact system, avoid interpreting a slow run as a model-wide speed ranking.
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The application reports an unsupported GPU
Recheck compute capability, driver version, operating system and backend documentation. Ollama’s requirements are hardware-family and version dependent, and AMD support follows ROCm-specific conditions.
Decision framework
- Want the lowest-risk first install: Qwen 3.5 4B, using a supported quantized build and moderate context.
- Want to explore more capability and can tolerate tuning: Test Qwen 3.5 9B from the 8–16GB guidance, with conservative context and close VRAM monitoring.
- Need a specific serving stack: Follow that stack’s own requirements. NVIDIA NIM’s Llama 3.1 8B figures cannot be substituted for requirements of a different runtime.
- Need the best result for a particular job: Build a small representative prompt set and select the model that is accurate and stable within your memory budget.
An 8GB card can run useful local language models, but the winning configuration is the one that leaves enough memory for your real context and runtime. Use Qwen 3.5 4B as the evidence-backed starting point, treat 9B options as experiments, and verify every model file and backend on the exact GPU you own.
Frequently Asked Questions
Which local LLM can I run with 8GB VRAM?
Qwen 3.5 4B is NVIDIA’s directly matched starting recommendation for RTX cards in the 6–8GB tier. Use a supported quantized file and leave room for context and runtime memory.
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It may, depending on quantization, context length, backend and CPU offload. NVIDIA places Qwen 3.5 9B in an 8–16GB llama.cpp tier, so treat it as a configuration to test rather than a guaranteed full-GPU fit.
Does an 8B model automatically need 8GB of VRAM?
No. Precision, quantization, context cache, runtime buffers and deployment stack all change memory use. NVIDIA’s NIM figures for Llama 3.1 8B range from 8GB minimum for fp8 to 16GB minimum for bf16.
Is 4-bit quantization always the best choice?
No. Lower precision usually saves memory, but aggressive quantization can reduce answer quality. Formats such as Q4_K_M and NVFP4 are backend- and model-specific, so compare supported files on your own prompts.
Why does a model load but crash on a long prompt?
The longer context increases key-value-cache memory and can consume the remaining VRAM. Lower the context, shorten retrieved text or use a smaller quantized build.
Can I use Ollama on any 8GB NVIDIA card?
Check the exact GPU and current Ollama requirements. Its documentation lists compute capability 5.0 or newer with driver 550 or newer, while cards from compute capability 5.0 through 6.2 need driver 570 or newer.
Should I choose the largest model that fits?
Not necessarily. NVIDIA recommends using the most powerful model that fits comfortably. A smaller model with headroom may be more reliable and responsive for your real prompts than a larger model relying on heavy offload.
The Bottom Line
For an 8GB graphics card, begin with a supported, quantized Qwen 3.5 4B build at a moderate context. Explore Qwen 3.5 9B only as a carefully monitored experiment, and verify the exact GPU, driver, backend and memory behavior on your workload.
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