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There is no single hardware requirement for an AI NPC mod. The decisive question is where the artificial-intelligence model runs. A hosted setup sends prompts to a remote service, so your computer mainly runs the game and the mod interface. A local setup runs the language model—and possibly speech recognition, text-to-speech, image analysis, retrieval, and behavior loops—on the same machine as the game. Local inference can work offline after installation, but it makes CPU time, GPU VRAM, system memory, and cooling part of the mod’s workload.
Hosted and local AI use different resources
Before estimating requirements, identify the mod’s inference mode. Some projects offer a choice; others are designed around one approach. A hosted model transfers text (and sometimes audio or images) over the internet to a provider. The provider performs inference and returns the response. Your PC still spends resources on game rendering, scripting, audio capture, networking, and displaying the result, but it does not need to hold the model weights.
Local inference keeps prompts and model execution on your computer. The game and the AI process then compete for the same CPU cores, GPU memory, system RAM, storage bandwidth, and thermal headroom. A local setup can continue without an internet connection once its model files, runtime, and dependencies are installed. The chasm project documentation describes its default mode as running entirely on the local machine, with no cloud, API keys, or account.
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|---|---|---|---|
| Hosted | Runs the game, mod code, input/output handling, and any optional local audio processing | Required for requests and responses | Lower local GPU and RAM demand, but latency, outages, provider limits, and privacy policies matter |
| Local | Runs the game plus model inference and any enabled local speech, vision, retrieval, or automation features | Not required after models and dependencies are installed, unless a feature calls a remote service | More privacy and offline capability, but shared CPU, VRAM, RAM, and power can reduce performance |
A hosted configuration therefore often leaves the local GPU requirement close to the base game’s requirement. A local configuration needs additional headroom beyond whatever the game already consumes.
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How much CPU does an AI NPC mod use?
There is no honest universal CPU percentage. Load changes with the model runtime, number of NPCs, prompt length, response length, token generation speed, and whether speech recognition or other processing is enabled. A documentation page for the Barotrauma AI NPC project explicitly says that local speech-to-text uses the CPU and “may be taxing on weaker computers.” That is a warning about a particular feature, not a fixed requirement for every AI NPC mod.
When CPU load is highest
- CPU-only inference: Some low-end configurations, including defaults documented by the chasm project, run the model without a GPU. This avoids VRAM use but can make generation contend directly with the game’s simulation and rendering-support threads.
- Local speech-to-text: Converting microphone input to text adds continuous or repeated CPU work. Enable it only when voice interaction is useful.
- Many simultaneous NPC turns: Parallel requests, background planning, memory, or reflection loops can keep several inference tasks active instead of allowing one response to finish before the next begins.
- Long contexts: Conversation history, retrieved documents, and large system prompts increase the amount of text the runtime must process.
CPU contention usually appears as rising frame times, simulation hitches, delayed input, or responses that arrive in bursts. A modern multi-core processor may handle a small text-only local model comfortably while struggling with CPU-only speech recognition and several autonomous NPCs at once. Because projects use different runtimes and scheduling strategies, a processor model or percentage copied from one project would not be a reliable minimum for another.
Hosted mode and CPU
With hosted text inference, the remote service performs the expensive model calculation. Your CPU still handles the game’s normal workload, network encryption, JSON or protocol parsing, and mod logic. Local voice capture or speech-to-text can remain the dominant AI-related CPU cost even when the language model itself is remote.
Do AI NPC mods use the GPU?
Hosted text inference generally does not require extra local GPU memory beyond the game. Local inference can use the GPU heavily, and the limiting resource is usually VRAM, not the graphics card’s marketing name. The game, textures, ray-tracing buffers, and model must all fit at the same time.
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Immersive AI’s Steam Workshop documentation recommends a GPU with 12–16 GB or more of VRAM for local models using native tool calling, paired with 32 GB of system RAM. RealTalk’s documentation gives a more graduated guide: 16 GB or more is comfortable, 12 GB is workable with care, 8 GB is tight, and below 8 GB should use the CPU or another machine. These are project recommendations, not laboratory benchmarks or universal minimums.
| Available VRAM | Practical interpretation for local AI NPCs | Adjustments likely needed |
|---|---|---|
| 16 GB or more | Comfortable starting point for demanding local, tool-calling configurations according to RealTalk | Still reserve memory for the game; high-resolution textures and ray tracing reduce headroom |
| 12 GB | Workable with care, and within Immersive AI’s 12–16 GB recommendation | Use a smaller or quantized model, reduce context length, and lower graphics settings if allocation fails |
| 8 GB | Tight for a game and local model sharing one GPU | Prefer a small quantized model, disable optional vision or other GPU features, or offload to CPU/another machine |
| Below 8 GB | RealTalk advises CPU inference or another computer | Expect slower generation and watch for system-RAM spillover |
Why VRAM disappears faster than expected
Model weights are only part of the allocation. The runtime also needs space for the context window, temporary activations, tool-calling state, and sometimes audio or image tensors. The game reserves VRAM for textures, geometry, frame buffers, and post-processing. Ray tracing and high-resolution texture settings can consume enough additional memory to turn a previously stable setup into an out-of-memory failure. Leaving headroom is safer than sizing the model to the card’s exact capacity.
How much RAM does an AI NPC mod need?
Immersive AI pairs its local recommendation with 32 GB of system RAM. Treat that as a comfortable target for demanding configurations, not a universal minimum. Actual use depends on model size, quantization, context length, runtime overhead, the game, and how much of the model cannot fit in VRAM.
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What increases RAM demand?
- Larger models: More parameters require more memory even before context and runtime buffers are counted.
- Quantization choice: Smaller quantized representations reduce memory use, usually with a quality or flexibility trade-off.
- Long conversations: Retained history and retrieved memories enlarge the context passed to each turn.
- Multiple agents: Separate NPC sessions, planners, or background memories can require separate state.
- Other applications: Browsers, recording software, overlays, and launchers reduce the RAM available to the game and model.
Close unnecessary applications, select a model that leaves room for the game, and monitor committed memory rather than relying only on the amount marked “free.” Virtual-memory paging can prevent an immediate failure but is far slower than keeping active model data in physical RAM.
How much internet bandwidth does an AI NPC mod use?
No defensible MB/s or GB-per-hour figure is published by the reviewed project documentation. Bandwidth depends on prompt and context size, response length, audio encoding, image or vision frequency, the provider’s protocol, and how many NPC turns occur. A text-only conversation may exchange relatively small request and response payloads, while voice, screenshots, vision frames, or generated music can add substantially more data.
| Feature | Network behavior | What determines usage |
|---|---|---|
| Hosted text chat | Uploads prompts and receives generated text | Context length, response length, number of turns, and protocol overhead |
| Hosted speech recognition or speech synthesis | Transfers audio in one or both directions | Sample rate, codec, duration, and whether audio is streamed or sent in clips |
| Vision | Uploads screenshots or video-derived images and receives text or actions | Image resolution, compression, capture frequency, and number of frames |
| Local-only inference | No inference traffic after installation | Updates, downloads, telemetry, or optional remote services may still connect if enabled |
Latency is often more important than raw throughput for dialogue. A stable connection with predictable round-trip time can feel better than a faster connection with packet loss or large jitter. Provider rate limits and outages can delay responses even when your connection has ample capacity.
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- Choose a representative play session, including ordinary dialogue, combat, and the maximum number of active NPCs you expect.
- Record the session length and note whether text, voice, vision, or other remote features were enabled.
- Check the operating system’s network-usage view for the mod or game process before and after the session. If traffic is aggregated, record the total for the game while keeping other downloads closed.
- Compare several sessions rather than treating one short conversation as an hourly rate. Longer context and different response lengths can change the result substantially.
- Use the mod’s request or token counters when available, but treat them as application-level counts; they may not include transport overhead or audio bytes.
Which features make an AI NPC mod heavier?
A text-only NPC that answers one request at a time is the lightest common design. Workload rises as features are added:
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- Speech-to-text (STT): Adds local CPU work when processed on the computer. The Barotrauma documentation specifically warns that this can tax weaker systems.
- Text-to-speech (TTS): Uses local CPU/GPU resources if synthesized locally, or network traffic and service latency if hosted.
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- Retrieval and memory: Adds storage reads, embedding or search work, and longer prompts.
- Autonomous action loops: Increase the number of model turns and tool calls, even when no new player message is entered.
- Concurrency: Several NPCs generating simultaneously multiply active state and can exhaust VRAM or CPU time faster than one NPC with a longer reply.
Disable features you do not need while establishing a stable baseline. Add voice, vision, memory, and autonomous behavior one at a time so you can identify which change causes frame-time spikes or delayed replies.
Practical hardware targets
The following targets combine the documented project guidance with the distinction between comfortable capacity and hard minimums.
| Use case | GPU/VRAM guidance | System RAM guidance | CPU and network considerations |
|---|---|---|---|
| Hosted text-only NPCs | Meet the base game’s GPU requirement; no universal AI-specific VRAM figure is established | Meet the base game and mod requirements; extra RAM depends on the game and other software | Stable connection and low latency matter more than a published bandwidth number |
| Small local quantized model | Choose a model that leaves visible headroom after the game starts; 12 GB can be workable with care | More than the base game may be needed, depending on model and context | Monitor CPU if using CPU offload or local speech recognition |
| Demanding local tool-calling setup | 16 GB or more is a comfortable target; Immersive AI cites 12–16 GB or more | 32 GB is a sensible comfortable target | Use a multi-core CPU and avoid running several heavy background applications |
| GPU below 8 GB | RealTalk recommends CPU inference or another machine | Enough RAM to hold the model and game, but spillover will slow responses | Expect lower generation speed and watch CPU temperatures and frame times |
None of these rows guarantees a particular frame rate or response speed. They describe capacity decisions documented by the named projects, not standardized benchmarks across games, drivers, operating systems, and model runtimes.
Setup checklist for a stable local configuration
- Confirm the execution mode. Read the mod’s settings or documentation to determine whether inference, STT, TTS, vision, and retrieval are local, hosted, or mixed.
- Start with a smaller quantized model. Verify that it loads while the game is running and that the runtime reports available VRAM rather than allocating the entire card.
- Leave graphics headroom. Lower texture resolution or disable ray tracing if the model and game together approach the VRAM limit.
- Keep context bounded. Set a practical conversation or memory limit so old dialogue does not grow without limit.
- Enable one optional feature at a time. Test local STT, TTS, vision, and autonomous loops separately.
- Watch temperatures and clocks. Sustained inference can expose cooling or power limits that a short game session does not.
- Record a baseline. Note frame time, RAM use, VRAM use, and response latency before changing settings.
Troubleshooting slow replies, stutter, and failures
Replies become much slower after loading a larger model
Check VRAM and system-RAM telemetry. If the model no longer fits in VRAM, it may have spilled into system RAM, the condition RealTalk associates with slower replies. Switch to a smaller quantized model, shorten the context, reduce graphics memory use, or move inference to another machine.
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The game stutters when voice input is enabled
Disable local speech recognition and test text input. The Barotrauma AI NPC documentation identifies local STT as CPU-intensive on weaker computers. If stutter disappears, keep STT off, use a lighter recognizer if the mod supports one, or use hosted recognition where its latency and privacy terms are acceptable.
The mod reports an out-of-memory error
Reduce model size or quantization memory, close other applications, lower texture or ray-tracing settings, and reduce context length. A card’s advertised VRAM is shared with the game; it is not all available to the model.
Hosted NPCs stop responding
Check connection stability, provider status, authentication, usage limits, and request latency. Measure traffic during a representative session rather than assuming a bandwidth number. If the mod supports a local fallback, test it with a smaller model before you need it.
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How to measure your own CPU, GPU, RAM, and network use
Because no standardized benchmark or bandwidth meter covers every AI NPC mod, your session is the most useful final check. Use the game’s frame-time graph to distinguish a rendering problem from a simulation hitch. In the operating system, monitor per-process CPU utilization, committed RAM, and page-file activity. GPU telemetry should show both utilization and dedicated VRAM allocation; a high utilization percentage alone does not tell you whether memory is exhausted. Record request latency and token counters from the mod when those are available, and pair them with the network-usage total for the same timed session.
Repeat the measurement with one NPC, then with your intended maximum concurrency. Test text-only first, then add voice, vision, retrieval, and autonomous behavior. This produces a workload-specific limit instead of turning a recommendation made for one project into a false minimum for every AI NPC mod.
What “offline” really means
A local model can generate NPC responses without an internet connection after installation, but the complete mod may still contact remote services for updates, telemetry, hosted STT or TTS, cloud authentication, or optional content. Review each feature’s configuration and test with the network disconnected before relying on offline play. Hosted inference, by definition, requires network access for every turn that uses the remote model.
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