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Short answer: a 27-billion-parameter model needs about 54 GB for FP16 weights, 27 GB at 8-bit, or 13.5 GB at 4-bit before runtime overhead. For practical local use, plan on a 20–24 GB graphics card for a Q4 quantization, at least 32 GB of system RAM for a mostly-GPU setup, and 64 GB if you expect CPU offload, long prompts, or other applications running at the same time. A 16 GB GPU can run some Q4 builds, but only with limited context, reduced cache settings, or partial offload.
What the 27B memory estimate actually means
“27B” means roughly 27 billion learned parameters. The first-order memory calculation multiplies that parameter count by the number of bytes used to store each parameter:
- FP16: 27 billion × 2 bytes ≈ 54 GB.
- 8-bit: 27 billion × 1 byte ≈ 27 GB.
- 4-bit: 27 billion × 0.5 byte ≈ 13.5 GB.
Those figures describe the weights alone, not a complete running process. A loader also needs memory for temporary tensors, allocator fragmentation, the operating system, model metadata, and the key-value (KV) cache that stores attention history for your prompt and generated text. Quantized formats add their own scale and metadata overhead. That is why a real 27B Q4_K_M GGUF file can be about 16.55 GB, rather than exactly 13.5 GB.
llama.cpp documentation makes the operational constraint explicit: models are fully loaded into memory, so you need enough disk space to save a model and enough RAM to load it. A model file that barely fits on a drive or in VRAM can still fail when the runtime requests its working buffers.
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How much VRAM does each quantization need?
| Weight format | Calculated weight memory for 27B | What that means in practice |
|---|---|---|
| FP16 | About 54 GB | Requires a 48 GB-class accelerator or multiple GPUs, with additional memory for cache and runtime allocations. |
| 8-bit | About 27 GB | Exceeds the capacity of common 24 GB cards once overhead and context are included; a larger accelerator or offload is normally required. |
| 4-bit | About 13.5 GB by arithmetic | A practical Q4_K_M file may be around 16.55 GB. A 20 GB card is a sensible target; 24 GB leaves more room for cache and buffers. |
Quantization changes memory use, speed, and sometimes output quality. Lower-bit files generally make local execution possible on consumer hardware, but the exact footprint depends on the quantization scheme, model architecture, and loader. Treat the arithmetic as a baseline, not a guaranteed allocation.
Recommended hardware tiers
| Hardware configuration | Expected fit | Recommended use |
|---|---|---|
| 16 GB VRAM + 32 GB RAM | Tight Q4 | Use shorter context, a smaller or lower-precision KV cache when supported, or partial CPU offload. Keep background applications closed and leave headroom for allocations. |
| 20 GB VRAM + 32 GB RAM | Practical Q4 | Matches the roughly 20 GB target for a 16.55 GB Q4_K_M file at ordinary context lengths. |
| 24 GB VRAM + 32–64 GB RAM | Comfortable Q4 | Provides useful room for KV cache, runtime buffers, and longer prompts. Choose 64 GB RAM when layers or cache may spill to the CPU. |
| 48 GB VRAM + 64 GB RAM | High precision or long context | Can accommodate larger quantizations and longer contexts, subject to the model architecture and software. |
| CPU-only with 64–128 GB RAM | Possible, but slower | Keep the complete quantized model in system memory. Generation speed depends strongly on CPU performance and memory bandwidth. |
The capacities are planning targets, not benchmark guarantees. No independent tokens-per-second result is established here; speed varies with processor, GPU, memory bandwidth, quantization, context length, batch settings, and framework.
How much system RAM do you need?
Mostly-GPU execution
32 GB of system RAM is a sensible floor when the model weights and most computations reside on the GPU. The computer still needs memory for the operating system, the loader, tokenizer data, file caches, and any applications open alongside the inference program. A 16.55 GB Q4 file does not mean the rest of the machine can be ignored.
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CPU offload or mixed GPU/CPU execution
Plan on 64 GB RAM when you will offload layers or KV-cache data to system memory. The system must hold the offloaded portion in addition to normal desktop use. If you intend to keep several model files loaded, use long contexts, or run other memory-heavy software, 128 GB gives substantially more operating room. Offload makes a model fit; it does not make it run at GPU-only speed.
CPU-only inference
A CPU-only setup needs enough RAM for the entire quantized model plus runtime overhead. For a Q4 27B model, 64 GB is a practical starting point and 128 GB is safer for large contexts and multitasking. The limiting resource then becomes CPU throughput and memory bandwidth, so capacity alone does not predict an acceptable response rate.
Can a 27B model run on a 16GB GPU?
Sometimes, but a 16 GB card leaves little margin for a real Q4 file of about 16.55 GB. You must change at least one variable:
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- Reduce context: a shorter maximum prompt lowers KV-cache allocation.
- Use partial CPU offload: move one or more layers, or cache data when the framework supports it, into system RAM.
- Choose a tighter quantization: a lower-bit build can reduce weight memory, with a possible quality trade-off.
- Use a smaller KV-cache precision: supported quantized cache modes reduce the additional VRAM consumed as context grows.
- Close competing GPU applications: browsers, games, recording tools, and other models can consume enough VRAM to cause an allocation failure.
Expect slower generation when offloading is active because data crosses between GPU and system memory. A 16 GB configuration is therefore a constrained compatibility option, not the recommended experience.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsIs 24GB VRAM enough for a Q4 27B model?
For ordinary context lengths, 24 GB VRAM is the most comfortable mainstream target for Q4. It can hold a roughly 16.55 GB Q4_K_M file while leaving several gigabytes for runtime buffers and KV cache. The remaining margin is not unlimited: increasing context, batch size, or parallel sequences consumes it progressively.
One deployment report describes a 24 GB GPU whose setup could not offload the KV cache and consequently limited the default context to around 8K tokens. That figure is framework- and model-specific, not a universal 24 GB limit. The same card may support a different context length with another architecture, cache precision, loader, or layer-placement strategy.
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Why context length and the KV cache change the answer
The model weights stay the same size when you increase the prompt, but the KV cache grows as the runtime retains attention information for more tokens. Larger maximum context, larger batch sizes, and multiple simultaneous conversations all raise VRAM demand.
If you run out of VRAM while loading
The weights and initial buffers do not fit. Lower the quantization, reduce the number of GPU-resident layers, or select a smaller model file. Do not assume that lowering context alone will recover enough memory for a file that already exceeds the card’s capacity.
If loading succeeds but generation fails at longer prompts
The KV cache is the likely pressure point. Reduce the context limit, lower the number of concurrent sequences, select a supported quantized KV cache, or offload cache data to system RAM. These changes can reduce speed and, depending on cache precision, may slightly affect output quality.
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If a nominal 8K or 16K setting is unstable
Context labels are maximum capacities, not promises that every hardware and software combination can sustain them. Start lower, verify a complete prompt-and-response cycle, and increase the limit in steps while monitoring VRAM and system RAM.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to configure a 27B model locally
- Choose the quantization. Start with a Q4 build if your goal is consumer-GPU inference. Keep an FP16 or 8-bit file only when you have the required accelerator memory.
- Check storage before downloading. Reserve space for the model file and at least one alternate quantization if you may need to change files. A Q4_K_M example is about 16.55 GB, so a 2 TB NVMe SSD provides room for the model, operating-system files, and other checkpoints without immediately filling the drive.
- Reserve system RAM. Use 32 GB as the floor for mostly-GPU use and 64 GB when offloading, increasing context, or multitasking.
- Start with a conservative context. Set a moderate context limit, load one conversation, and observe peak VRAM rather than relying on idle usage.
- Place as many layers on the GPU as fit. In llama.cpp-style runners this is commonly controlled by a GPU-layer setting. Leave headroom instead of forcing every layer into the last available megabytes.
- Increase context gradually. After a stable short run, raise the context limit and test a long prompt. Stop when allocation errors, swapping, or severe slowdowns appear.
- Use CPU offload deliberately. If the model does not fit, move layers or cache to RAM and expect lower tokens per second. Monitor both GPU memory and system memory during generation.
Storage, operating-system headroom, and failure symptoms
- “Out of memory” during load: the file plus initial allocations exceed available VRAM or RAM. Use a smaller quantization, fewer GPU layers, or more system memory.
- Loads only after closing applications: competing processes were consuming the headroom. Keep several gigabytes free rather than sizing to the model file exactly.
- System starts swapping: RAM is insufficient for offloaded layers, cache, and desktop workloads. Reduce context or add RAM; swapping can make generation unusably slow.
- Very slow generation after offload: this is the expected bandwidth penalty of moving tensors between CPU memory and VRAM. It is a fit solution, not a performance upgrade.
- Model file fits the SSD but the loader cannot open it: free space is not the same as contiguous working space. Keep additional disk capacity for temporary files, alternate quantizations, and updates.
Choosing between 16GB, 20GB, and 24GB GPUs
| GPU capacity | Best-case 27B Q4 use | Main compromise |
|---|---|---|
| 16 GB | Short context with aggressive memory management | Little headroom; likely partial offload or lower-bit cache, with slower generation. |
| 20 GB | Ordinary Q4 context | Less room than 24 GB for unusually long prompts, large batches, or background GPU workloads. |
| 24 GB | Comfortable Q4 and more context flexibility | Still architecture- and framework-dependent; very long contexts may require cache offload. |
| 48 GB | Higher precision, larger cache, or long-context experimentation | Higher hardware cost and no guarantee of a specific tokens-per-second rate. |
What performance should you expect?
Memory capacity determines whether the model can load; it does not determine response speed by itself. Tokens per second depend on the CPU and GPU models, memory bandwidth, quantization implementation, context length, batch size, number of GPU-resident layers, and the inference framework. A fully GPU-resident Q4 model will generally avoid the transfer penalty of CPU offload, but the available evidence does not provide an independently tested benchmark for any particular hardware combination. Treat vendor or community speed claims as configuration-specific rather than a universal 27B result.
A practical buying and build checklist
- Target GPU: 20 GB minimum for a practical Q4 setup; 24 GB preferred for context and runtime headroom.
- Target RAM: 32 GB for mostly-GPU use; 64 GB for offload, long context, or multitasking; 128 GB for CPU-only work with generous headroom.
- Storage: leave substantially more free space than the 16.55 GB example file so alternate quantizations and temporary data fit.
- Software: use a loader that supports your model format, GPU backend, layer offload, and any quantized KV-cache mode you plan to use.
- Testing: measure peak VRAM and RAM during a long prompt, not just at startup.
- Fallback: prepare a lower-bit file or a smaller context limit before you need it.
Bottom line for common configurations
A 27B model is straightforward on a 48 GB-class accelerator at higher precision, but most local users will choose a Q4 file. For that use case, 20 GB VRAM is workable, 24 GB is more comfortable, and 32 GB system RAM is the practical floor. Choose 64 GB RAM when CPU offload or larger contexts matter. A 16 GB GPU can work only by accepting tighter context, lower-bit settings, or partial offload, and CPU-only operation is possible with 64–128 GB RAM at a substantial speed cost.
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