Short answer: a 27-billion-parameter model needs about 54GB for FP16 weights, 27GB at 8-bit, or 13.5GB at 4-bit before the memory used by the operating system, runtime buffers and the KV cache. For a practical local installation, plan on a 20–24GB graphics card for a Q4 quantization, at least 32GB of system RAM for a mostly-GPU setup, and 64GB if you expect CPU offload, long prompts or other applications running at the same time.
A 16GB GPU can run some 27B Q4 builds, but it leaves almost no room for context and allocator overhead. You will usually need a shorter context, a lower-bit quantization, partial CPU offload, or a combination of those compromises. The exact limit depends on the model architecture, file format, inference framework and context length.
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The memory a 27B model needs at a glance
Parameter count gives the first estimate. Multiply 27 billion parameters by the bytes used to store each parameter:
| Weight format | Bytes per parameter | Approximate weight memory | What that means locally |
|---|---|---|---|
| FP16 | 2 | 54GB | Requires workstation-class GPU memory or CPU loading; little room remains on a 48GB card. |
| 8-bit | 1 | 27GB | Generally needs a GPU larger than 24GB once runtime memory is included. |
| 4-bit | 0.5 | 13.5GB | The practical consumer option, but the actual model file and runtime footprint are larger. |
These are decimal, first-order estimates rather than guaranteed allocation sizes. A real Q4_K_M 27B GGUF example is about 16.55GB on disk, not 13.5GB, because quantization metadata, tensor packing and file structure add space. Loading also requires allocator buffers, temporary workspaces, the operating system and the KV cache.
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That is why a 20GB card is a sensible minimum target for ordinary Q4 use, while 24GB provides useful headroom. The model file is only one part of the memory budget.
How the estimate is derived
Weights are the baseline
Every parameter must be represented in memory. At FP16, each parameter occupies roughly two bytes, so 27 billion multiplied by two is approximately 54GB. An 8-bit representation halves that to roughly 27GB. A 4-bit representation halves it again to roughly 13.5GB.
Quantization reduces weight storage by using fewer bits for each value. It does not make all other memory requirements disappear. The inference engine still needs buffers for matrix operations, the model loader, temporary calculations and the conversation state.
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Why the file is larger than the arithmetic
Quantized formats include scales, zero points, headers and other metadata. Different quantization schemes also pack values differently. The cited 27B Q4_K_M file at approximately 16.55GB illustrates the gap between a theoretical 13.5GB weight calculation and a real downloadable artifact. Leave free disk space for the model file plus any alternate quantizations you may test; a 2TB NVMe SSD is a practical capacity class for several large models, caches and operating-system files.
How much RAM do I need for a 27B model?
Mostly-GPU use: 32GB is a sensible floor
With nearly all layers on the GPU, 32GB of system RAM gives the loader and operating system reasonable working room. It also prevents a browser, editor or background service from competing with the model during startup. This is a capacity recommendation, not a claim that every framework will consume 32GB.
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GPU use with CPU offload: 64GB is safer
CPU offload moves some layers, buffers or the KV cache from VRAM into system memory. A 64GB machine is a better target for this arrangement because it leaves room for the offloaded portion, the operating system and normal multitasking. The amount actually required depends on how many layers are moved and on context length.
CPU-only operation: 64–128GB
A CPU-only installation must keep the complete quantized model and its runtime allocations in system memory. A 64GB system can run a Q4 model in some configurations, but 128GB gives substantially more margin for larger contexts, temporary allocations and other applications. Generation will generally be much slower than GPU inference because speed is constrained by CPU compute and memory bandwidth.
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llama.cpp documentation notes that models are fully loaded into memory and therefore require adequate disk space and sufficient RAM to load them. The practical implication is simple: do not size RAM to the model file alone.
VRAM and RAM combinations that make sense
| Hardware | Likely result | Best-use guidance |
|---|---|---|
| 16GB VRAM + 32GB RAM | Tight Q4 fit | Use a shorter context, a smaller KV cache, lower-bit weights or partial CPU offload. Keep several gigabytes uncommitted for runtime allocations. |
| 20GB VRAM + 32GB RAM | Practical Q4 fit | Matches the roughly 16.55GB Q4_K_M example with ordinary context lengths and modest overhead. |
| 24GB VRAM + 32–64GB RAM | Comfortable Q4 fit | Provides more room for KV cache and buffers. Choose 64GB RAM if layers or cache may be offloaded. |
| 48GB VRAM + 64GB RAM | Higher precision or longer context | Can accommodate larger quantizations and longer contexts, subject to the model architecture and framework. |
| CPU only with 64–128GB RAM | Possible, but slower | Keep the full quantized model in system memory and expect performance to depend heavily on CPU and memory bandwidth. |
Can a 27B model run on a 16GB GPU?
Yes, but not as a carefree, all-on-GPU installation. A Q4_K_M file around 16.55GB leaves almost no VRAM for the KV cache, CUDA or other runtime allocations. Out-of-memory errors may appear while loading, or the model may load but fail when a longer prompt expands the cache.
Ways to make 16GB work
- Reduce context length. A smaller maximum context creates a smaller KV cache.
- Use a supported quantized KV cache. This lowers cache memory, with possible quality or compatibility trade-offs.
- Offload part of the model to system RAM. This frees VRAM but usually lowers generation speed.
- Choose a lower-bit or more compact quantization. Memory falls, while output quality and stability can change.
- Close memory-heavy applications. Desktop capture, games, browsers and other GPU workloads can consume the headroom the model needs.
Expect to tune settings rather than select a single number and forget it. A configuration that handles a short question may fail on a long document because the cache grows with every additional token.
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Is 24GB VRAM enough for Q4?
For many 27B Q4 builds, 24GB is the most comfortable consumer capacity. It leaves several gigabytes beyond a 16.55GB-class file for runtime buffers and a normal conversation. It is not a universal guarantee: a larger context, a different model architecture, full-precision cache or framework-specific workspace can consume the remaining margin.
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How context length and the KV cache change the requirement
The model weights remain the same size when you change context length, but the KV cache grows as the prompt and generated conversation become longer. The cache stores intermediate attention information for prior tokens so the engine does not recompute the entire history each time.
Why a short test can mislead you
A model may load successfully with a small context and then run out of memory when you increase the maximum context or paste a long document. The cache allocation can be reserved up front by some frameworks or expand during use in others. Check both behaviors in the documentation for your chosen runtime.
What to change when VRAM is full
- Lower the maximum context and retry the same prompt.
- Select a supported lower-precision KV-cache mode if your runtime provides one.
- Move some layers or the cache to system RAM.
- Use a smaller quantization only after confirming that context tuning is not enough.
One deployment report describes a 24GB GPU being limited to around 8K context when KV-cache offload was unavailable. Treat that figure as specific to that model and framework, not as a universal 24GB limit. Architecture, cache precision and software version can produce a different result.
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CPU offload: what it solves and what it costs
CPU offload divides the model between VRAM and system RAM. Layers that do not fit on the GPU remain in RAM and are processed through the CPU or transferred across the system bus. Some runtimes can also place the KV cache in system memory.
- Benefit: a model larger than your graphics card can still load.
- Cost: transfers and CPU work usually reduce tokens per second and can increase latency.
- Memory requirement: the offloaded layers and their buffers need additional system RAM beyond the operating system baseline.
- Best practice: leave unused RAM so the operating system does not start swapping to disk.
Offload is a capacity solution, not a performance upgrade. If fast interactive responses matter, maximize the number of layers that remain on the GPU while reserving enough VRAM for the cache and runtime.
Comparing FP16, 8-bit and Q4 for local use
| Choice | Memory baseline | Typical hardware implication | Trade-off |
|---|---|---|---|
| FP16 | About 54GB for weights | Large-memory workstation GPU or substantial CPU RAM | Highest weight precision, but difficult to fit on consumer GPUs. |
| 8-bit | About 27GB for weights | More than 24GB VRAM once overhead is included, or mixed GPU/CPU placement | Less memory than FP16 while retaining more precision than Q4. |
| Q4 | About 13.5GB in the first-order estimate; around 16.55GB for the cited Q4_K_M file | 20–24GB VRAM is the practical class; 16GB is tight | Best capacity-to-quality compromise for many local users, with quantization-dependent quality differences. |
There is no single “Q4” size. Q4_K_M and other variants use different packing and metadata, so compare the actual file size and format supplied for the model you intend to run.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Expected speed and what controls it
Tokens-per-second figures cannot be stated responsibly without a specific GPU or CPU, memory bandwidth, runtime, context length, batch size and offload arrangement. No independent benchmark is established here. In general, keeping more layers and the cache on a fast GPU is faster than CPU offload; CPU-only execution is usually the slowest option. Longer contexts can also reduce throughput because the cache and attention workload grow.
When comparing two machines, record the quantization, context limit, KV-cache precision and number of offloaded layers along with any speed result. A headline tokens-per-second number without those conditions is not an apples-to-apples comparison.
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A practical setup checklist
- Choose the quantization first. For a 27B model, start by checking the exact Q4 file size. The cited example is approximately 16.55GB.
- Reserve VRAM for more than weights. Subtract the model file size from your card capacity and keep the remainder for the runtime and KV cache.
- Set system RAM according to placement. Use 32GB as a mostly-GPU floor, 64GB when offload or multitasking is expected, and up to 128GB for comfortable CPU-only use.
- Decide the initial context limit. Start conservatively, verify that the model loads and handles a representative prompt, then increase context in small steps.
- Test the longest real workload. A short chat does not prove that a long document or multi-turn session will fit.
- Keep storage headroom. Store the model on a fast SSD and leave room for alternate quantizations, temporary files and updates.
- Adjust one variable at a time. Change context, KV-cache precision, quantization or offload placement separately so you can identify which setting solved an out-of-memory error.
Common failure symptoms and fixes
The loader reports out of memory before generation
The weights plus startup buffers exceed available VRAM. Use a smaller quantization, reduce GPU layers through CPU offload, or move to a card with more capacity.
The model loads, then fails on a long prompt
The KV cache consumed the remaining headroom. Lower context, enable a supported quantized cache, or place the cache in system RAM if the runtime supports it.
The computer becomes unresponsive during CPU offload
System RAM may be exhausted and the operating system may be swapping. Close other applications, reduce the offloaded workload or add RAM. Disk swapping is far slower than keeping working data in memory.
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Generation is unexpectedly slow
More layers may have been assigned to the CPU than expected, or the workload may be limited by memory bandwidth. Confirm placement, context length and cache mode before judging the model or hardware.
Quick Recap
Bottom line for each common hardware target
- 16GB VRAM: possible with Q4, but expect tuning, shorter context or partial offload.
- 20GB VRAM: workable Q4 target for ordinary context lengths.
- 24GB VRAM: the comfortable choice for Q4, with useful room for cache and runtime overhead.
- 32GB system RAM: sensible for mostly-GPU operation.
- 64GB system RAM: safer for CPU offload, longer contexts and multitasking.
- 64–128GB without a suitable GPU: CPU-only Q4 is possible, but speed will depend strongly on processor and memory bandwidth.
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