How Much Memory to Leave for an AI Assistant When Streaming at 1080p

First, calculate what's already in use

An AI assistant doesn't work in a vacuum: it shares hardware with the game, the encoder, and the stream itself. Before allocating memory, calculate how much the other participants are already consuming. At 1080p, the main load falls on the encoder — hardware on the GPU or software on the CPU — plus the game itself and screen capture.

A practical guideline: if the game and encoder together take up a significant share of video memory, the free remainder will go to the model, not to cache — and you'll get stutters not from a lack of memory, but from its constant reallocation. So don't think "how much to give" but "how much is left after the game and encoder."

Local model or cloud

This is the main fork in the road, and everything depends on it.

A cloud assistant barely uses local memory: requests go over the network, and only the client remains on your machine. The question then isn't about gigabytes but about channel stability and latency — during an active stream, upload is already busy, and the assistant shares it with video. If the channel is narrow, the assistant will respond with a delay or compete with the stream for bandwidth.

A local model keeps its weights in video memory or RAM permanently. Here, memory is a direct expense that must be set aside in advance. But there are no network delays and upload stays free.

How much video memory a local model requires

Exact numbers depend on the model and quantization, but you can calculate using the formula: number of parameters multiplied by storage bit depth. At 4-bit quantization, each billion parameters takes up a little over half a gigabyte — plus overhead for service buffers and context cache. Hence the working VRAM guidelines:

• a model with 3–4 billion parameters at 4 bits — about 2–3 GB;
• 7–8 billion parameters at 4 bits — roughly 4.5–5.5 GB;
• 13–14 billion parameters at 4 bits — around 8–9 GB;
• 30–34 billion parameters at 4 bits — about 18–20 GB.

Add context cache to these numbers: it grows with dialogue length and can add several gigabytes on top during long conversations. Full precision instead of quantization increases weight volume by 3–4 times and during a 1080p stream almost always means there won't be any video memory left for the game.

Which 7–8 billion parameter models to choose for streaming

The 7–8B class at 4-bit quantization is the working middle ground for builds with 12–16 GB VRAM. For a streamer's chat assistant, people look at instruct versions of families like Qwen, Llama, Mistral, Gemma: they calculate predictably using the formula above and fit within the stated volumes. The difference between them is in the quality of Russian-language responses and generation speed, not in memory requirements — the parameter count is roughly the same.

For an assistant that reads chat and suggests reactions with short replies, 7–8B with moderate context is sufficient. Models at 3–4B are noticeably weaker in coherent text but free up 2–3 GB for the game. Full, non-quantized weights at 7–8B will take up several times more and won't fit the budget during a 1080p stream.

VRAM shortage and offload: how it changes the calculation

If the weights don't fit in video memory, some layers go to system memory via offload — and the model keeps working. The price is speed: data transfers over the bus, inference slows down, and both RAM and VRAM are consumed simultaneously. Offload doesn't eliminate the calculation — it moves the expense from VRAM to RAM and reduces response speed.

So "load a bigger model and enable offload" during an active stream is a compromise on the edge: the assistant will think longer, and the system will start swapping during game peaks. It's better to take a smaller model and fit it entirely into the free remainder.

RAM

If the model runs on CPU, the expense moves to RAM: the same half-gigabyte per billion parameters at 4 bits plus buffers. A 7–8B model on the processor will take about 5–6 GB of RAM on top of system, game, and encoder needs, and CPU inference is noticeably slower — the assistant will respond with a noticeable pause. Software 1080p encoding itself loads the processor, and stacking inference on the same CPU is a sure way to get drops.

Why you can't give all free memory

The temptation is simple: there's this much free — I'll give it all to the model. That's a mistake. Memory is needed as a buffer for peaks: asset loading in the game, bitrate spikes in the encoder, background system work. If the model takes the remainder, the system will start swapping — and you'll get freezes whose source you won't immediately find. Peaks in a stream come at the most inconvenient moments — during dynamic scenes and location changes.

How to measure memory usage at peak

Task Manager averages will deceive you: you need the peak during combat, not a calm screen.

• nvidia-smi with looping refresh shows used VRAM and the total limit per process;
• MSI Afterburner with RivaTuner overlay displays used VRAM and GPU load right on top of the game — convenient for tracking peaks visually;
• built-in statistics or driver overlay gives the same values without third-party programs;
• Task Manager and Resource Monitor are needed for RAM and swap — there you can see when the system started paging out.

Look at the maximum for the session, not the average: that's what determines what's left for the model.

How to choose the volume for your build

1. Launch the game and encoder in combat mode at 1080p, measure the peak via nvidia-smi or Afterburner overlay.
2. Subtract the peak from the available volume — you get the upper bound for the model with headroom for peaks.
3. Launch the assistant, monitor frames, swap, and response time.
4. If freezes appear — first shorten the context, then reduce the model class or enable more aggressive quantization, and repeat until drops and swap disappear.

The method requires repeated measurements: peaks vary by game and scene.

What actually saves memory

If memory is in short supply, there are several levers, each with its own cost:

• shorter context and history reset reduce cache usage, but you lose the thread of the conversation;
• a smaller model or more aggressive quantization saves gigabytes, but response quality drops;
• a cloud assistant frees local memory entirely, but adds latency and network dependency;
• switching 1080p to a hardware encoder relieves the CPU and often gives more than fine-tuning gigabytes;
• limiting response length and history size in client settings keeps cache usage predictable.

Start with the encoder and context: these are the cheapest measures in terms of quality. Touch quantization and model downsizing when these steps aren't enough.