Token size if planning to use LLM while running a game?

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Absolute beginner. First post about my first project.

Trying to decide on the appropriate model to use for what will essentially be a chatbot running while I’m in a game. A quick google search says my 16GB VRAM Nvidia 4080 and 64GB of RAM could handle something thats 14-22B, should I dial it back to something like Mistral 7B if the game itself recommends a 1070 (8GB VRAM)?

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It’s probably best not to exhaust all your VRAM for gaming, so I recommend starting by testing LLM models around 3B to 8B with Ollama ,etc.

To visualize the size: For the Q4_K_M format used in GGUF compression by Ollama or Llama.cpp, an 8B model fits within about 5GB. Even including working memory, 8GB of VRAM is sufficient… Remembering it as “8GB for an 8B model in Q4_K_M” makes it easier.

“Token size” to plan for when an LLM runs while you’re gaming

When people say “tokens,” they usually mean context window length (how many tokens the model keeps in memory for prompt + chat history + new output). That choice matters because the model stores a KV cache that grows roughly linearly with tokens. Hugging Face’s Transformers docs summarize KV cache as the mechanism that speeds generation but consumes memory as sequences get longer. (Hugging Face)

A good beginner target

  • Start at 4,096 tokens context
  • Move to 8,192 tokens only if you confirm you still have VRAM headroom and no stutter
  • Cap output (e.g., 128–256 new tokens) to keep generation smooth

This matches what popular local runtimes assume: Ollama’s default context is 4096 tokens, and it documents how to override it (e.g., OLLAMA_CONTEXT_LENGTH=8192 or num_ctx). (Ollama)

Why 4k is a sensible default (VRAM math, simplified)

A practical estimate for Llama-3-class 8B KV cache is about 128 KB per token (FP16 KV), from a detailed inference sizing guide. (VMware Blogs)

That implies KV cache alone is approximately:

  • 4096 tokens → ~0.5 GiB
  • 8192 tokens → ~1.0 GiB
  • 16384 tokens → ~2.0 GiB (VMware Blogs)

That’s in addition to the model weights and runtime overhead. While gaming, you want KV cache to stay modest.

Why “game recommends GTX 1070 (8GB)” doesn’t mean you can safely spend the other 8GB on an LLM

Modern engines often use extra GPU memory as cache when it’s available. For example, Unity’s texture streaming docs explicitly note that if you have extra memory, you can set a larger budget so Unity can keep more texture data in a GPU cache. (Unity)

So on an RTX 4080, a game may use more VRAM than it would on an 8GB card, which is why the safe approach is to:

  1. Measure your game’s VRAM usage (after loading into a typical area)
  2. Leave 2–3 GB VRAM headroom to avoid spikes/stutter
  3. Fit the LLM into what’s left

Model size guidance for your exact hardware (RTX 4080 16GB + game)

Best “LLM + game on one GPU” range

  • 7–8B models (quantized) are the sweet spot
  • 12B can work if the game is light or you lower settings
  • 14–22B is often doable for LLM alone, but becomes fragile while gaming unless you accept aggressive offload/slowdowns

Good Hugging Face models for an in-game chatbot (practical picks)

Recommended starting model (strong + efficient)

Qwen/Qwen3-8B (Instruct)

  • The Qwen3-8B config shows GQA-style KV heads (num_key_value_heads: 8) and max_position_embeddings: 40960 (so it supports long context, but you still shouldn’t use huge context while gaming). (Hugging Face)
  • Official GGUF repo exists for easy local running: Qwen/Qwen3-8B-GGUF. (Hugging Face)

Why it fits your case: excellent quality per VRAM, easy to run in GGUF tooling, and modern architecture.

Very stable option if your game is heavy on VRAM

microsoft/Phi-4-mini-instruct (3.8B)

  • Small enough to be hard to “break” while gaming.
  • Model card states 128K context capability (again, you’d typically run 4k–8k locally). (Hugging Face)

A classic, widely supported 7B option

mistralai/Mistral-7B-Instruct-v0.3

  • Model card notes v0.3 updates like function calling support and tokenizer changes. (Hugging Face)

If you want “bigger,” newest practical 12B option (Windows-friendly)

nvidia/Mistral-Nemo-12B-Instruct-ONNX-INT4 (Feb 2026)

  • NVIDIA states it’s an INT4 quantized ONNX model using TensorRT Model Optimizer. (Hugging Face)
  • This is the kind of packaging that can make 12B more realistic on a single 16GB GPU, but it still depends heavily on how much VRAM the game uses.

If you want INT4 for 7B as well (same ecosystem)

nvidia/Mistral-7B-Instruct-v0.3-ONNX-INT4 (Feb 2026) (Hugging Face)

Best setups for your case (beginner-friendly → more DIY)

Setup A (easiest): LM Studio + GGUF (recommended to start)

  • LM Studio runs llama.cpp (GGUF) models on Windows/macOS/Linux. (LM Studio)

  • llama.cpp requires models in GGUF format. (GitHub)

  • Download Qwen/Qwen3-8B-GGUF (Hugging Face) and pick a 4-bit quant (common choice: Q4_K_M; an example repo lists ~5.03 GB for Q4_K_M). (Hugging Face)

  • Set:

    • Context: 4096
    • Max output: 192–256
    • Keep chat history short (summarize older turns)

Why this works well while gaming: predictable VRAM usage and minimal setup complexity.

Setup B (simple CLI/API): Ollama

  • Default context is 4096 and docs show how to change it via env var or num_ctx. (Ollama)
  • You can also import a GGUF from Hugging Face via a Modelfile (FROM /path/to/file.gguf). (Ollama)

Good if: you want a local HTTP API quickly for your game/tooling.

Setup C (Python integration): llama-cpp-python

  • n_gpu_layers controls how many layers are offloaded to GPU (e.g., -1 means “all layers”). (Llama CPP Python)
  • Useful if your project is Python-based and you want tight control over prompt construction and state.

Setup D (Windows performance path): ONNX INT4 (NVIDIA)

  • Use NVIDIA’s published INT4 ONNX models (e.g., Mistral-NeMo 12B INT4). (Hugging Face)
  • Typically best if you’re willing to follow a more “deployment-like” workflow.

What I’d do in your exact situation (most likely to succeed)

  1. Start with Qwen3-8B in GGUF (Q4)

  2. Run at 4096 context, output cap 192–256

    • Keep memory stable; KV cache stays modest (see KV sizing). (VMware Blogs)

  3. If you get stutter/OOM:

    • Drop to Phi-4-mini-instruct (3.8B) (Hugging Face)
    • Or reduce context/output further (e.g., 2048 context, 128 output)

  4. Only if you confirm the game is using low VRAM and you have headroom:

Practical “token budget” for an in-game chatbot

For most NPC/chatbot use:

  • System prompt + character + rules: 300–800 tokens
  • Recent conversation: 500–1500 tokens
  • Game state summary injected each turn: 100–400 tokens
  • Total target: ~2k–4k tokens (start here)

If you later need “long memory,” don’t jump to 32k/128k; instead:

  • keep short context and store long-term memory externally (notes/summaries/vector DB), injecting only the relevant bits.
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This is awesome. Thanks for taking the time to hold a noobs hand walk me through it. Much appreciated.

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